Methods and materials for identifying malignant skin lesions

ABSTRACT

This document provides methods and materials for identifying malignant skin lesions (e.g., malignant pigmented skin lesions). For example, methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify malignant skin lesions are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 61/726,217, filed Nov. 14, 2012. The disclosure of the priorapplication is considered part of (and is incorporated by reference in)the disclosure of this application.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted in ASCII format via electronic filing and is herebyincorporated by reference in its entirety. Said ASCII copy, created onAug. 7, 2013, is named sequence_listing.txt and is 101,321 bytes insize.

BACKGROUND

1. Technical Field

This document relates to methods and materials for identifying malignantskin lesions (e.g., malignant pigmented skin lesions). For example, thisdocument relates to methods and materials for using quantitative PCRresults and correction protocols to reduce the impact of basalkeratinocyte contamination on the analysis of test sample results toidentify malignant skin lesions.

2. Background Information

Malignant skin lesions are typically identified by obtaining a skinbiopsy and morphologically assessing the biopsy's melanocytes under amicroscope. Such a procedure can be difficult to standardize and canlead to overcalling of melanomas.

Once a diagnosis of melanoma is made by morphological assessment, therisk of metastasis is typically determined by the invasion depth ofmalignant cells into the skin (i.e., the Breslow depth). The Breslowdepth can dictate further work-up such as a need for an invasivesentinel lymph node (SLN) procedure. Such procedures, however, can leadto inaccurate determinations of the true malignant potential of apigmented lesion.

SUMMARY

This document provides methods and materials for identifying malignantskin lesions (e.g., malignant pigmented skin lesions). For example, thisdocument provides methods and materials for using quantitative PCRresults and correction protocols to reduce the impact of basalkeratinocyte contamination on the analysis of test sample results toidentify malignant skin lesions.

As described herein, quantitative PCR can be performed using a routineskin biopsy sample (e.g., a paraffin-embedded tissue biopsy) to obtainexpression data (e.g., gene copy numbers) for one or more marker genes.Correction protocols can be used to reduce the impact of basalkeratinocyte contamination on the analysis of the expression data fromthe test sample. For example, the contribution of gene expression frombasal keratinocytes present within the test skin sample can bedetermined and removed from the overall gene expression values todetermine the final gene expression value for a particular gene asexpressed from cells other than basal keratinocytes (e.g., melanocytes).An assessment of the final gene expression values, which includeminimal, if any, contribution from basal keratinocytes, for a collectionof marker genes can be used to determine the benign or malignantbiological behavior of the tested skin lesion.

In general, one aspect of this document features a method foridentifying a malignant skin lesion. The method comprises, or consistsessentially of, (a) determining, within a test sample, the expressionlevel of a marker gene selected from the group consisting of PLAT, SPP1,TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1,PTK2, PLOD3, ITGA3, IL8, and CXCL1 to obtain a measured expression levelof the marker gene for the test sample, (b) determining, within the testsample, the expression level of a keratinocyte marker gene to obtain ameasured expression level of the keratinocyte marker gene for the testsample, (c) removing, from the measured expression level of the markergene for the test sample, a level of expression attributable tokeratinocytes present in the test sample using the measured expressionlevel of the keratinocyte marker gene for the test sample and akeratinocyte correction factor to obtain a corrected value of markergene expression for the test sample, and (d) identifying the test sampleas containing a malignant skin lesion based, at least in part, on thecorrected value of marker gene expression for the test sample. Thekeratinocyte marker gene can be K14. The marker gene can be SPP1. Thestep (c) can comprise (i) multiplying the measured expression level ofthe keratinocyte marker gene for the test sample by the keratinocytecorrection factor to obtain a correction value and (ii) subtracting thecorrection value from the measured expression level of the marker genefor the test sample to obtain the corrected value of marker geneexpression for the test sample. Unless otherwise defined, all technicaland scientific terms used herein have the same meaning as commonlyunderstood by one of ordinary skill in the art to which this inventionpertains. Although methods and materials similar or equivalent to thosedescribed herein can be used in the practice or testing of the presentinvention, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from thefollowing detailed description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart of an exemplary process for determining the geneexpression value, which includes minimal, if any, contribution frombasal keratinocytes, for a marker gene by cells within a tested sample(e.g., a tested skin biopsy sample).

FIG. 2 is a flow chart of an exemplary process for determining akeratinocyte correction factor for a marker gene of interest.

FIG. 3 is a flow chart of an exemplary process for removing copy numbercontamination from basal keratinocytes from a copy number value for amarker gene to determine the gene expression value, which includesminimal, if any, contribution from basal keratinocytes, for that markergene by cells within a tested sample (e.g., a tested skin biopsysample).

FIG. 4 is a diagram of an example of a generic computer device and ageneric mobile computer device that can be used as described herein.

FIG. 5 is a flow chart of an exemplary process for using FN1 and SPP1expression levels to determine the benign or malignant nature of a skinlesion.

FIG. 6 is a flow chart of an exemplary process for using FN1 and ITGB3expression levels to determine the benign or malignant nature of a skinlesion.

FIG. 7 is a network diagram.

DETAILED DESCRIPTION

This document provides methods and materials for identifying malignantskin lesions (e.g., malignant pigmented skin lesions). For example, thisdocument provides methods and materials for using quantitative PCRresults and correction protocols to reduce the impact of basalkeratinocyte contamination on the analysis of test sample results toidentify malignant skin lesions.

FIG. 1 shows an exemplary process 100 for determining a gene expressionvalue, which includes minimal, if any, contribution from basalkeratinocytes, for a marker gene by cells within a tested sample (e.g.,a tested skin biopsy sample). The process begins at box 102, wherequantitative PCR using a collection of primer sets and a test sample isused to obtain a Ct value for the target of each primer set. Each geneof interest can be assessed using a single primer set or multipledifferent primer sets (e.g., two, three, four, five, six, seven, or moredifferent primer sets). In some cases, quantitative PCR is performedusing each primer set and control nucleic acid of the target of eachprimer set (e.g., linearized cDNA fragments) to obtain a standard curvefor each primer set as set forth in box 104. In some cases, quantitativePCR is performed using each primer set and a known sample as an internalcontrol (e.g., a stock biological sample) to obtain an internal controlvalue for each primer set as set forth in box 106. This internal controlcan be used to set values for each primer set across different assays.In some cases, the quantitative PCR performed according to boxes 102,104, and 106 can be performed in parallel. For example, the quantitativePCR performed according to boxes 102, 104, and 106 can be performed in asingle 96 well format.

At box 108, the quality of the obtained standard curves can beconfirmed. In some cases, a gene of interest included in the assayformat can be a melanocyte marker (e.g., levels of MLANA and/or MITFexpression) to confirm the presence of melanocytes in the test sample.Other examples of melanocyte markers that can be used as describedherein include, without limitation, TYR, TYRP1, DCT, PMEL, OCA2, MLPH,and MC1R.

At box 110, the raw copy number of each target present in the testsample is determined using the Ct values and the standard curve for eachtarget. In some cases, the averaged, corrected copy number for each geneis calculated using the raw copy number of each target of a particulargene and the internal control value for each primer set (box 112). Thisaveraged, corrected copy number value for each gene can be normalized toa set number of one or more housekeeping genes as set forth in box 114.For example, each averaged, corrected copy number value for each genecan be normalized to 100,000 copies of the combination of ACTB, RPL8,RPLP0, and B2M. Other examples of housekeeping genes that can be used asdescribed herein include, without limitation, RRN18S, GAPD, PGK1, PPIA,RPL13A, YWHAZ, SDHA, TFRC, ALAS1, GUSB, HMBS, HPRT1, TBP, and TUPP. Oncenormalized, the copy number values for each gene can be referred to asthe averaged, corrected, normalized copy number for that gene as presentin the test sample.

At box 116, the averaged, corrected, normalized copy number for eachgene can be adjusted to remove the copy number contamination from basalkeratinocytes present in the test sample. In general, copy numbercontamination from basal keratinocytes can be removed by (a) determininga keratinocyte correction factor for the gene of interest using one ormore keratinocyte markers (e.g., keratin 14 (K14)) and one or morenormal skin samples (e.g., FFPE-embedded normal skin samples), (b)determining the averaged, corrected, normalized copy number value forthe one or more keratinocyte markers of the test sample and multiplyingthat value by the keratinocyte correction factor to obtain a correctionvalue for the gene of interest, and (c) subtracting that correctionvalue from the averaged, corrected, normalized copy number value of thegene of interest to obtain the final copy number for the gene ofinterest. Examples of keratinocyte markers that can be used as describedherein include, without limitation, KRTS, KRT1, KRT10, KRT17, ITGB4,ITGA6, PLEC, DST, and COL17A1.

With reference to FIG. 2, process 200 can be used to obtain akeratinocyte correction factor for a gene of interest. At box 202, theaveraged, corrected, normalized copy number for one or more genes ofinterest (e.g., Gene X) and one or more basal keratinocyte marker genes(e.g., K14) are determined using one or more normal skin samples andprocedures similar to those described in FIG. 1. As box 204, thekeratinocyte correction factor for each gene of interest (e.g., Gene X)is determined by dividing the averaged, corrected, normalized copynumber for each gene of interest present in a normal skin sample by theaveraged, corrected, normalized copy number of a basal keratinocytemarker gene present in a normal skin sample. Examples of keratinocytecorrection factors for particular genes of interest are set forth inTable E under column “AVG per copy K14.”

With reference to FIG. 3, once a keratinocyte correction factor indetermined for a particular gene of interest (e.g., Gene X), then theaveraged, corrected, normalized copy number for the basal keratinocytemarker gene present in the test sample can be multiplied by thekeratinocyte correction factor for the gene of interest (e.g., Gene X)to obtain a correction value for the gene of interest (e.g., Gene X).See, e.g., box 302. At box 304, the correction value for the gene ofinterest (e.g., Gene X) is subtracted from the averaged, corrected,normalized copy number for the gene of interest (e.g., Gene X) presentin the test sample to obtain a final copy number value of the gene ofinterest (e.g., Gene X) present in the test sample.

FIG. 4 is a diagram of an example of a generic computer device 1400 anda generic mobile computer device 1450, which may be used with thetechniques described herein. Computing device 1400 is intended torepresent various forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. Computing device 1450 isintended to represent various forms of mobile devices, such as personaldigital assistants, cellular telephones, smart phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be exemplary only, andare not meant to limit implementations of the inventions describedand/or claimed in this document.

Computing device 1400 includes a processor 1402, memory 1404, a storagedevice 1406, a high-speed interface 1408 connecting to memory 1404 andhigh-speed expansion ports 1410, and a low speed interface 1415connecting to low speed bus 1414 and storage device 1406. Each of thecomponents 1402, 1404, 1406, 1408, 1410, and 1415, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 1402 can processinstructions for execution within the computing device 1400, includinginstructions stored in the memory 1404 or on the storage device 1406 todisplay graphical information for a GUI on an external input/outputdevice, such as display 1416 coupled to high speed interface 1408. Inother implementations, multiple processors and/or multiple buses may beused, as appropriate, along with multiple memories and types of memory.Also, multiple computing devices 1400 may be connected, with each deviceproviding portions of the necessary operations (e.g., as a server bank,a group of blade servers, or a multi-processor system).

The memory 1404 stores information within the computing device 1400. Inone implementation, the memory 1404 is a volatile memory unit or units.In another implementation, the memory 1404 is a non-volatile memory unitor units. The memory 1404 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 1406 is capable of providing mass storage for thecomputing device 1400. In one implementation, the storage device 1406may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described herein. The information carrier is a computer- ormachine-readable medium, such as the memory 1404, the storage device1406, memory on processor 1402, or a propagated signal.

The high speed controller 1408 manages bandwidth-intensive operationsfor the computing device 1400, while the low speed controller 1415manages lower bandwidth-intensive operations. Such allocation offunctions is exemplary only. In one implementation, the high-speedcontroller 1408 is coupled to memory 1404, display 1416 (e.g., through agraphics processor or accelerator), and to high-speed expansion ports1410, which may accept various expansion cards (not shown). In theimplementation, low-speed controller 1415 is coupled to storage device1406 and low-speed expansion port 1414. The low-speed expansion port,which may include various communication ports (e.g., USB, Bluetooth,Ethernet, or wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,an optical reader, a fluorescent signal detector, or a networking devicesuch as a switch or router, e.g., through a network adapter.

The computing device 1400 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 1420, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 1424. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 1422. In some cases, components from computing device 1400 maybe combined with other components in a mobile device (not shown), suchas device 1450. Each of such devices may contain one or more ofcomputing device 1400, 1450, and an entire system may be made up ofmultiple computing devices 1400, 1450 communicating with each other.

Computing device 1450 includes a processor 1452, memory 1464, aninput/output device such as a display 1454, a communication interface1466, and a transceiver 1468, among other components (e.g., a scanner,an optical reader, a fluorescent signal detector). The device 1450 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 1450,1452, 1464, 1454, 1466, and 1468, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 1452 can execute instructions within the computing device1450, including instructions stored in the memory 1464. The processormay be implemented as a chipset of chips that include separate andmultiple analog and digital processors. The processor may provide, forexample, for coordination of the other components of the device 1450,such as control of user interfaces, applications run by device 1450, andwireless communication by device 1450.

Processor 1452 may communicate with a user through control interface1458 and display interface 1456 coupled to a display 1454. The display1454 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid CrystalDisplay) or an OLED (Organic Light Emitting Diode) display, or otherappropriate display technology. The display interface 1456 may compriseappropriate circuitry for driving the display 1454 to present graphicaland other information to a user. The control interface 1458 may receivecommands from a user and convert them for submission to the processor1452. In addition, an external interface 1462 may be provide incommunication with processor 1452, so as to enable near areacommunication of device 1450 with other devices. External interface 1462may provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 1464 stores information within the computing device 1450. Thememory 1464 can be implemented as one or more of a computer-readablemedium or media, a volatile memory unit or units, or a non-volatilememory unit or units. Expansion memory 1474 may also be provided andconnected to device 1450 through expansion interface 1472, which mayinclude, for example, a SIMM (Single In Line Memory Module) cardinterface. Such expansion memory 1474 may provide extra storage spacefor device 1450, or may also store applications or other information fordevice 1450. For example, expansion memory 1474 may include instructionsto carry out or supplement the processes described herein, and mayinclude secure information also. Thus, for example, expansion memory1474 may be provide as a security module for device 1450, and may beprogrammed with instructions that permit secure use of device 1450. Inaddition, secure applications may be provided via the SIMM cards, alongwith additional information, such as placing identifying information onthe SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described herein. The information carrier is acomputer- or machine-readable medium, such as the memory 1464, expansionmemory 1474, memory on processor 1452, or a propagated signal that maybe received, for example, over transceiver 1468 or external interface1462.

Device 1450 may communicate wirelessly through communication interface1466, which may include digital signal processing circuitry wherenecessary. Communication interface 1466 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 1468. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 1470 mayprovide additional navigation- and location-related wireless data todevice 1450, which may be used as appropriate by applications running ondevice 1450.

Device 1450 may also communicate audibly using audio codec 1460, whichmay receive spoken information from a user and convert it to usabledigital information. Audio codec 1460 may likewise generate audiblesound for a user, such as through a speaker, e.g., in a handset ofdevice 1450. Such sound may include sound from voice telephone calls,may include recorded sound (e.g., voice messages, music files, etc.) andmay also include sound generated by applications operating on device1450.

The computing device 1450 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 1480. It may also be implemented as part of asmartphone 1482, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,and Programmable Logic Devices (PLDs)) used to provide machineinstructions and/or data to a programmable processor, including amachine-readable medium that receives machine instructions as amachine-readable signal. The term “machine-readable signal” refers toany signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the systems and techniquesdescribed herein can be implemented on a computer having a displaydevice (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display)monitor) for displaying information to the user and a keyboard and apointing device (e.g., a mouse or a trackball) by which the user canprovide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback (e.g., visualfeedback, auditory feedback, or tactile feedback); and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

The systems and techniques described herein can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed herein), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Marker Genes that Discriminate Between Benign andMalignant Tissue

Marker genes were ordered by their ability to differentiate benign frommalignant tissue (Table A). This was based on the analysis of 73 benignand 53 malignant tissues, and the hypothesis that changes in expressionof fibronectin-associated gene networks are indicative of malignant cellbehavior. Values of the test statistic were for the Wilcoxon rank sumtest. The values of the test statistic for a Winsorized two-sample test(trimmed outliers were replaced with actual values) and for thechi-square test for the zero vs. >zero versions of each variable wereincluded. The top 5 discriminatory genes based on each statistical testwere highlighted in bold.

TABLE A Test statistic value Wilcoxon Winsorized rank sum two-sampleChi- gene test t-test square test FN1 −10.2312 −8.04081 106.714 SPP1−9.0279 −4.9374 86.774 COL4A1 −8.8807 −7.27171 83.711 TNC −8.7511−8.31049 75.549 ITGA3 −8.6008 −5.86334 79.788 LOXL3 −8.1978 −6.7532775.144 AGRN −8.1243 −7.91238 62.611 VCAN −8.0812 −6.24088 67.388 PLOD3−8.0384 −6.89248 62.691 ITGB1 −8.0021 −7.38143 59.973 PTK2 −7.5279−7.19889 54.446 CTGF −7.4997 −5.581 57.79 PLOD1 −7.332 −7.36126 44.87LAMC1 −7.2425 −6.1057 54.233 THBS1 −7.2425 −5.60331 54.233 LOXL2 −7.2241−6.33208 55.909 IL6 −7.1777 −6.41883 56.966 LOXL1 −7.1279 −6.3443152.878 IL8 −7.1194 −5.76042 57.296 CYR61 −6.741 −6.97388 43.866 ITGAV−6.5947 −6.27571 47.021 YAP −6.4848 −6.36431 42.417 BGN −6.3419 −6.0106625.387 LAMB1 −6.3293 −5.68826 37.061 ITGB3 −6.3142 −5.13158 40.835 CXCL1−6.1077 −5.66564 40.137 THBS2 −6.0427 −5.02003 37.413 COL18A1 −6.0379−4.9125 41.339 SPARC −6.0272 −6.39324 38.098 TP53 −6.0182 −6.1855434.945 PLOD2 −5.9082 −3.50272 47.576 CCL2 −5.8844 −5.38758 30.69 FBLN2−5.5848 −4.59826 31.913 LAMA1 −5.4876 −4.2817 31.071 THBS4 −5.3971−3.88786 35.27 COL1A1 −5.325 −4.37617 34.693 ITGA5 −4.9847 −3.5669525.243 TAZ −4.036 −3.26011 18.313 POSTN −3.8054 −2.78378 19.813 LOX−3.728 −2.8677 17.157 CSRC −3.7078 −3.71759 13.983 LAMA3 −3.5805−2.99652 13.391 CDKN1A −3.5766 −3.20447 17.228 CDKN2A −3.5491 −2.9090315.938 ITGA2 −3.4083 −2.72495 11.766 LAMC2 −3.4083 −2.53784 11.766PCOLCE2 −3.3469 −3.53676 14.449 LOXL4 −3.2079 −2.76128 10.943 PCOLCE−2.2172 −1.13805 7.993 LAMB3 −1.2822 0.89459 7.028 CSF2 2.175 1.930954.522

Example 2 Marker Panel Revision after Statistical Analysis

The candidate gene list from Example 1 was modified to include other FN1network genes as well as four housekeeping genes (ACTB, RPLP0, RPL8, andB2M), two keratinocyte markers (K10 and K14) to assess keratinocytecontamination, and four melanocyte markers (MITF, TYR, MLANA and PMEL)to assess melanocyte content in the skin sections. Genes from Example 1with low discriminatory value and a more distant neighborhood to FN1were excluded from the test setup (LAMC1, LOXL2, CYR61, YAP, BGN, LAMB1,THBS2, COL18A1, SPARC, TP53, PLOD2, CCL2, FBLN2, LAMA1, THBS4, COL1A1,TAZ, POSTN, LOX, CSRC, LAMA3, CDKN1A, CDKN2A, LAMC2, PCOLCE2, LOXL4,PCOLCE, LAMB3, and CSF2). Instead, the discriminatory ability of otherFN1 network genes was determined (PLAT, CSK, GDF15, FARP1, ARPC1B, NES,NTRK3, SNX17, L1CAM, and CD44). The following results were based on theanalysis of 26 benign nevi and 52 primary cutaneous melanomas withdocumented subsequent metastasis or skin lesions of melanoma metastasis(Table B). The top 5 genes were highlighted.

TABLE B Test Statistic value Wilcoxon Winsorized rank sum two-sampleChi-square gene test t-test test COL4A1 −5.85975 −5.42545 46.3273 FN1−5.50862 −3.63639 35.1951 PLAT −4.82670 −3.13568 25.7234 IL8 −4.61443−4.41668 28.6000 SPP1 −4.60153 −3.08137 23.0816 PLOD3 −4.37001 −3.9155318.8036 TNC −4.26431 −3.14128 19.5000 CXCL1 −4.24452 −3.76681 20.6471CSK −4.15178 −2.96444 18.3962 GDF15 −4.01364 −2.99752 13.7083 ITGB3−3.92608 −2.80068 16.3091 CCL2 −3.61870 −3.45423 17.5176 VCAN −3.46906−2.26781 12.5593 ITGB1 −3.40897 −3.63399 5.0221 PLOD1 −3.40380 −3.203099.2625 CTGF −3.11725 −2.20507 10.0645 THBS1 −3.11721 −2.01257 10.0645ITGA3 −3.04915 −2.65398 7.5341 FARP1 −2.99724 −2.28024 9.2857 AGRN−2.92104 −3.30679 1.8838 IL6 −2.85960 −3.05600 10.6257 LOXL3 −2.84999−2.70498 5.1096 LOXL1 −2.69957 −2.11477 8.1250 ARPC1B −2.57571 −2.82320All but 1 value >0 NES −2.45264 −2.70056 2.4375 PTK2 −2.22328 −2.261804.4057 ITGA2 −2.08353 −1.50078 4.4571 ITGA5 −1.93478 −1.39663 3.8451ITGAV −1.29341 −0.81964 3.5615 NTRK3 −1.22485 75 of the 78 values are =0 MITF 0.58305 0.73916 0.4274 SNX17 0.74754 0.90733 0.0785 L1CAM 1.611250.27151 2.1081 MLANA 2.96258 2.92548 All values >0 CD44 5.23089 7.17590All but 1 value >0

Based on the results of Example 1 and above, FN1 was identified as acomponent of the melanoma phenotype that is at the core of a genenetwork that discriminates between benign and malignant melanocytic skinlesions (FIG. 7). The modeling was based on the STRING 9.0 database(string-db.org).

The list of all 71 genes tested is provided in Table 1.

TABLE 1 List of genes used to discriminate benign skin tissue lesionsfrom malignant skin tissue lesions. GenBank ® GenBank ® Gene NameAccession No. GI No. FN1 NM_212482 47132556 NM_002026 47132558 NM_21247447132548 NM_212476 47132552 NM_212478 47132554 NM_054034 47132546 SPP1NM_001040058 91206461 NM_001040060 91598938 NM_000582 38146097 COL4A1NM_001845 148536824 TNC NM_002160 340745336 ITGA3 NM_005501 171846264NM_002204 171846266 LOXL3 NM_032603 22095373 AGRN NM_198576 344179122VCAN NM_004385 255918074 NM_001164098 255918078 NM_001164097 255918076PLOD3 NM_001084 62739167 ITGB1 NM_002211 182519230 NM_133376 182507162NM_033668 182507160 PTK2 NM_001199649 313851043 NM_005607 313851042NM_153831 313851041 CTGF NM_001901 98986335 PLOD1 NM_000302 324710986LAMC1 NM_002293 145309325 THBS1 NM_003246 40317625 LOXL2 NM_00231867782347 IL6 NM_000600 224831235 LOXL1 NM_005576 67782345 IL8 NM_000584324073503 CYR61 NM_001554 197313774 ITGAV NM_001144999 223468594NM_001145000 223468596 NM_002210 223468593 YAP NM_001130145 303523503NM_001195045 303523626 NM_006106 303523510 NM_001195044 303523609 BGNNM_001711 268607602 LAMB1 NM_002291 167614503 ITGB3 NM_000212 47078291CXCL1 NM_001511 373432598 THBS2 NM_003247 40317627 COL18A1 NM_030582110611234 NM_130445 110611232 SPARC NM_003118 365777426 TP53 NM_000546371502114 NM_001126112 371502115 NM_001126114 371502117 NM_001126113371502116 PLOD2 NM_182943 62739164 NM_000935 62739165 CCL2 NM_00298256119169 FBLN2 NM_001998 51873054 NM_001004019 51873052 NM_001165035259013546 LAMA1 NM_005559 329112585 THBS4 NM_003248 291167798 COL1A1NM_000088 110349771 ITGA5 NM_002205 56237028 TAZ NM_000116 195232764NM_181311 195232766 NM_181312 195232765 NM_181313 195232767 POSTNNM_001135934 209862910 NM_006475 209862906 NM_001135935 209863010 LOXNM_001178102 296010939 NM_002317 296010938 CSRC NM_005417 38202215NM_198291 38202216 LAMA3 NM_198129 38045909 NM_001127717 189217424CDKN1A NM_000389 310832422 NM_001220777 334085239 NM_078467 310832423NM_001220778 334085241 CDKN2A NM_000077 300863097 NM_058195 300863095NM_001195132 304376271 ITGA2 NM_002203 116295257 LAMC2 NM_005562157419137 NM_018891 157419139 PCOLCE2 NM_013363 296317252 LOXL4NM_032211 67782348 PCOLCE NM_002593 157653328 LAMB3 NM_000228 62868214NM_001017402 62868216 NM_001127641 189083718 CSF2 NM_000758 371502128ACTB NM_001101 168480144 RPLP0 NM_053275 49087137 NM_001002 49087144RPL8 NM_000973 72377361 NM_033301 15431305 B2M NM_004048 37704380 K10NM_000421 195972865 K14 NM_000526 197313720 MITF NM_198158 296841082NM_198177 296841080 NM_006722 296841079 NM_198159 296841078 NM_000248296841081 NM_001184967 296841084 NM_198178 296923803 TYR NM_000372113722118 MLANA NM_005511 5031912 PMEL NM_001200054 318037594NM_001200053 318037592 NM_006928 318068057 NES NM_006617 38176299 L1CAMNM_024003 221316758 NM_001143963 221316759 NM_000425 221316755 GDF15NM_004864 153792494 ARPC1B NM_005720 325197176 FARP1 NM_005766 48928036NM_001001715 159032536 NTRK3 NM_001007156 340745351 NM_001012338340745349 NM_001243101 340745352 NM_002530 340745350 CSK NM_001127190187475372 NM_004383 187475371 CD44 NM_001001391 48255940 NM_00100139248255942 NM_001202556 321400139 NM_001001389 48255936 NM_000610 48255934NM_001001390 48255938 NM_001202555 321400137 NM_001202557 321400141SNX17 NM_014748 388596703 PLAT NM_000930 132626665 NM_033011 132626641

Gene expression of target genes was assessed by SYBR/EVA-Green basedRT-PCR. All tested genes were accompanied by a standard curve forquantification of absolute copy number per a defined number ofhousekeeping genes. mRNA extraction from paraffin-embedded biospecimenwas performed using an extraction protocol (Qiagen RNA FFPE extractionkit) and an extraction robot (Qiacube from Qiagen). mRNA was transcribedinto cDNA using a commercially available kit (iScript kit from BioRad),and Fluidigm technology was used for PCR cycling.

The primer design was performed using web-based open access software.The primers were HPLC purified to minimize background and were optimizedfor formalin-fixed, paraffin-embedded (FFPE) tissue (i.e., highlydegraded tissue). The primers were designed to detect a maximum numberof gene transcripts and were designed to be cDNA specific (i.e., notaffected by genomic DNA contamination of the total, tissue-derivedcDNA). The housekeeping genes, keratin genes, melanocyte-specific genes,and selected high interest genes were detected using four separate andindividually designed primer pairs. The primer pairs are set forth inTable 2.

TABLE 2 Primer sets for indicated genes. Gene Name Forward primerReverse primer ACTB 5′-GCCAACCGCGAGAAGATG-3′;  5′-GGCTGGGGTGTTGAAGGT-3′;  SEQ ID NO: 1 SEQ ID NO: 25′-CGCGAGAAGATGACCCAGAT-3′;  5′-GGGGTGTTGAAGGTCTCAAA-3′; SEQ ID NO: 3SEQ ID NO: 4 5′-TGACCCAGATCATGTTTGAGA-3′; 5′-GTACATGGCTGGGGTGTTG-3′; SEQ ID NO: 5 SEQ ID NO: 6 5′-CTGAACCCCAAGGCCAAC-3′; 5′-TGATCTGGGTCATCTTCTCG-3′; SEQ ID NO: 7 SEQ ID NO: 8 RPLP05′-AACTCTGCATTCTCGCTTCC-3′;  5′-GCAGACAGACACTGGCAACA-3′; SEQ ID NO: 9SEQ ID NO: 10 5′-GCACCATTGAAATCCTGAGTG-3′; 5′-GCTCCCACTTTGTCTCCAGT-3′;SEQ ID NO: 11 SEQ ID NO: 12 5′-TCACAGAGGAAACTCTGCATTC-3′;5′-GGACACCCTCCAGGAAGC-3′;  SEQ ID NO: 13 SEQ ID NO: 145′-ATCTCCAGGGGCACCATT-3′;  5′-AGCTGCACATCACTCAGGATT-3′; SEQ ID NO: 15SEQ ID NO: 16 RPL8 5′-ACTGCTGGCCACGAGTACG-3′; 5′-ATGCTCCACAGGATTCATGG-3′; SEQ ID NO: 17 SEQ ID NO: 185′-ACAGAGCTGTGGTTGGTGTG-3′;  5′-TTGTCAATTCGGCCACCT-3′;  SEQ ID NO: 19SEQ ID NO: 20 5′-TATCTCCTCAGCCAACAGAGC-3′; 5′-AGCCACCACACCAACCAC-3′; SEQ ID NO: 21 SEQ ID NO: 22 5′-GTGTGGCCATGAATCCTGT-3′; 5′-CCACCTCCAAAAGGATGCTC-3′; SEQ ID NO: 23 SEQ ID NO: 24 B2M5′-TCTCTCTTTCTGGCCTGGAG-3′;  5′-GAATCTTTGGAGTACGCTGGA-3′; SEQ ID NO: 25SEQ ID NO: 26 5′-TGGAGGCTATCCAGCGTACT-3′; 5′-CGTGAGTAAACCTGAATCTTTGG-3′; SEQ ID NO: 27 SEQ ID NO: 285′-CCAGCGTACTCCAAAGATTCA-3′; 5′-TCTCTGCTGGATGACGTGAG-3′; SEQ ID NO: 29SEQ ID NO: 30 5′-GGCTATCCAGCGTACTCCAA-3′;  5′-GCTGGATGACGTGAGTAAACC-3′;SEQ ID NO: 31 SEQ ID NO: 32 KRT14 5′-ACCATTGAGGACCTGAGGAA-3′; 5′-GTCCACTGTGGCTGTGAGAA-3′; SEQ ID NO: 33 SEQ ID NO: 345′-CATTGAGGACCTGAGGAACA-3′;  5′-AATCTGCAGAAGGACATTGG-3′; SEQ ID NO: 35SEQ ID NO: 36 5′-GATGACTTCCGCACCAAGTA-3′;  5′-CGCAGGTTCAACTCTGTCTC-3′;SEQ ID NO: 37 SEQ ID NO: 38 5′-TCCGCACCAAGTATGAGACA-3′; 5′-ACTCATGCGCAGGTTCAACT-3′; SEQ ID NO: 39 SEQ ID NO: 40 KRT105′-GAGCCTCGTGACTACAGCAA-3′;  5′-GCAGGATGTTGGCATTATCAGT-3′; SEQ ID NO: 41SEQ ID NO: 42 5′-AAAACCATCGATGACCTTAAAAA-3′; 5′-GATCTGAAGCAGGATGTTGG-3′;SEQ ID NO: 43 SEQ ID NO: 44 MITF 5′-TTCCCAAGTCAAATGATCCAG-3′;5′-AAGATGGTTCCCTTGTTCCA-3′; SEQ ID NO: 45 SEQ ID NO: 465′-CGGCATTTGTTGCTCAGAAT-3′;  5′-GAGCCTGCATTTCAAGTTCC-3′; SEQ ID NO: 47SEQ ID NO: 48 TYR 5′-TTCCTTCTTCACCATGCATTT-3′; 5′-GGAGCCACTGCTCAAAAATA-3′; SEQ ID NO: 49 SEQ ID NO: 505′-TCCAAAGATCTGGGCTATGA-3′;  5′-TTGAAAAGAGTCTGGGTCTGAA-3′; SEQ ID NO: 51SEQ ID NO: 52 MLANA 5′-GAGAAAAACTGTGAACCTGTGG-3′;5′-ATAAGCAGGTGGAGCATTGG-3′; SEQ ID NO: 53 SEQ ID NO: 545′-GAAGACGAAATGGATACAGAGC-3′; 5′-GTGCCAACATGAAGACTTTTATC-3′;SEQ ID NO: 55 SEQ ID NO: 56 PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′; 5′-CCAAGGCCTGCTTCTTGAC-3′;  SEQ ID NO: 57 SEQ ID NO: 585′-GCTGTGGTCCTTGCATCTCT-3′;  5′-GCTTCATAAGTCTGCGCCTA-3′; SEQ ID NO: 59SEQ ID NO: 60 FN1 5′-CTCCTGCACATGCTTTGGA-3′;  5′-AGGTCTGCGGCAGTTGTC-3′; SEQ ID NO: 61 SEQ ID NO: 62 5′-AGGCTTTGGAAGTGGTCATT-3′; 5′-CCATTGTCATGGCACCATCT-3′; SEQ ID NO: 63 SEQ ID NO: 645′-GAAGTGGTCATTTCAGATGTGATT-3′; 5′-CCATTGTCATGGCACCATCT-3′;SEQ ID NO: 65 SEQ ID NO: 66 5′-TGGTCATTTCAGATGTGATTCAT-3′;5′-CATTGTCATGGCACCATCTA-3′; SEQ ID NO: 67 SEQ ID NO: 68 SPP15′-GTTTCGCAGACCTGACATCC-3′;  5′-TCCTCGTCTGTAGCATCAGG-3′; SEQ ID NO: 69SEQ ID NO: 70 5′-CCTGACATCCAGTACCCTGA-3′;  5′-TGAGGTGATGTCCTCGTCTG-3′;SEQ ID NO: 71 SEQ ID NO: 72 5′-GAATCTCCTAGCCCCACAGA-3′; 5′-GGTTTCTTCAGAGGACACAGC-3′; SEQ ID NO: 73 SEQ ID NO: 745′-CCCATCTCAGAAGCAGAATCTC-3′; 5′-ACAGCATTCTGTGGGGCTA-3′;  SEQ ID NO: 75SEQ ID NO: 76 COL4A 1 5′-GGAAAACCAGGACCCAGAG-3′; 5′-CTTTTTCCCCTTTGTCACCA-3′;  SEQ ID NO: 77 SEQ ID NO: 785′-AGAAAGGTGAACCCGGAAAA-3′; 5′-GGTTTGCCTCTGGGTCCT-3′;  SEQ ID NO: 79SEQ ID NO: 80 5′-GAGAAAAGGGCCAAAAAGGT-3′; 5′-CATCCCCTGAAATCCAGGTT-3′;SEQ ID NO: 81 SEQ ID NO: 82 5′-AAAGGGCCAAAAAGGTGAAC-3′;5′-CCTGGCATCCCCTGAAAT-3′;  SEQ ID NO: 83 SEQ ID NO: 84 TNC5′-GTGTCAACCTGATGGGGAGA-3′;  5′-GTTAACGCCCTGACTGTGGT-3′; SEQ ID NO: 85SEQ ID NO: 86 5′-GGTACAGTGGGACAGCAGGT-3′;  5′-GATCTGCCATTGTGGTAGGC-3′;SEQ ID NO: 87 SEQ ID NO: 88 5′-AACCACAGTCAGGGCGTTA-3′; 5′-GTTCGTGGCCCTTCCAGT-3′;  SEQ ID NO: 89 SEQ ID NO: 905′-AAGCTGAAGGTGGAGGGGTA-3′;  5′-GAGTCACCTGCTGTCCCACT-3′; SEQ ID NO: 91SEQ ID NO: 92 ITGA3 5′-TATTCCTCCGAACCAGCATC-3′; 5′-CACCAGCTCCGAGTCAATGT-3′; SEQ ID NO: 93 SEQ ID NO: 945′-CCACCATCAACATGGAGAAC-3′;  5′-AGTCAATGTCCACAGAGAACCA-3′; SEQ ID NO: 95SEQ ID NO: 96 LOXL3 5′-CAACTGCCACATTGGTGATG-3′; 5′-AAACCTCCTGTTGGCCTCTT-3′; SEQ ID NO: 97 SEQ ID NO: 985′-TGACATCACGGATGTGAAGC-3′;  5′-GGGTTGATGACAACCTGGAG-3′; SEQ ID NO: 99SEQ ID NO: 100 AGRN 5′-TGTGACCGAGAGCGAGAAG-3′; 5′-CAGGCTCAGTTCAAAGTGGTT-3′; SEQ ID NO: 101 SEQ ID NO: 1025′-CGGACCTTTGTCGAGTACCT-3′;  5′-GTTGCTCTGCAGTGCCTTCT-3′; SEQ ID NO: 103SEQ ID NO: 104 VCAN 5′-GACTTCCGTTGGACTGATGG-3′; 5′-TGGTTGGGTCTCCAATTCTC-3′; SEQ ID NO: 105 SEQ ID NO: 1065′-ACGTGCAAGAAAGGAACAGT-3′;  5′-TCCAAAGGTCTTGGCATTTT-3′; SEQ ID NO: 107SEQ ID NO: 108 PLOD3 5′-GCAGAGATGGAGCACTACGG-3′; 5′-CAGCCTTGAATCCTCATGC-3′;  SEQ ID NO: 109 SEQ ID NO: 1105′-GGAAGGAATCGTGGAGCAG-3′;  5′-CAGCAGTGGGAACCAGTACA-3′; SEQ ID NO: 111SEQ ID NO: 112 ITGB1 5′-CTGATGAATGAAATGAGGAGGA-3′;5′-CACAAATGAGCCAAATCCAA-3′; SEQ ID NO: 113 SEQ ID NO: 1145′-CAGTTTGCTGTGTGTTTGCTC-3′;  5′-CATGATTTGGCATTTGCTTTT-3′;SEQ ID NO: 115 SEQ ID NO: 116 PTK2 5′-GCCCCACCAGAGGAGTATGT-3′; 5′-AAGCCGACTTCCTTCACCA-3′;  SEQ ID NO: 117 SEQ ID NO: 1185′-GAGACCATTCCCCTCCTACC-3′;  5′-GCTTCTGTGCCATCTCAATCT-3′; SEQ ID NO: 119SEQ ID NO: 120 CTGF 5′-CGAAGCTGACCTGGAAGAGA-3′; 5′-TGGGAGTACGGATGCACTTT-3′; SEQ ID NO: 121 SEQ ID NO: 1225′-GTGTGCACCGCCAAAGAT-3′;  5′-CGTACCACCGAAGATGCAG-3′; SEQ ID NO: 123SEQ ID NO: 124 PLOD1 5′-CTACCCCGGCTACTACACCA-3′; 5′-GACAAAGGCCAGGTCAAACT-3′; SEQ ID NO: 125 SEQ ID NO: 1265′-AGTCGGGGTGGATTACGAG-3′;  5′-ACAGTTGTAGCGCAGGAACC-3′; SEQ ID NO: 127SEQ ID NO: 128 LAMC1 5′-ATGATGATGGCAGGGATGG-3′; 5′-GCATTGATCTCGGCTTCTTG-3′; SEQ ID NO: 129 SEQ ID NO: 130 THBS15′-CTGTGGCACACAGGAAACAC-3′;  5′-ACGAGGGTCATGCCACAG-3′;  SEQ ID NO: 131SEQ ID NO: 132 5′-GCCAAAGACGGGTTTCATTA-3′;  5′-GCCATGATTTTCTTCCCTTC-3′;SEQ ID NO: 133 SEQ ID NO: 134 LOXL2 5 ′-CTCCTCCTACGGCAAGGGA-3′; 5′-TGGAGATTGTCTAACCAGATGGG-3′; SEQ ID NO: 135 SEQ ID NO: 1365′-CTCCTACGGCAAGGGAGAAG-3′;  5′-TTGCCAGTACAGTGGAGATTG-3′; SEQ ID NO: 137SEQ ID NO: 138 1L6 5′-CCAGAGCTGTGCAGATGAGT-3′; 5′-TGCATCTAGATTCTTTGCCTTTT-3′; SEQ ID NO: 139 SEQ ID NO: 140 LOXL15′-AGGGCACAGCAGACTTCCT-3′;  5′-TCGTCCATGCTGTGGTAATG-3′; SEQ ID NO: 141SEQ ID NO: 142 5′-GCATGCACCTCTCATACCC-3′;  5′-CGCATTGTAGGTGTCATAGCA-3′;SEQ ID NO: 143 SEQ ID NO: 144 1L8 5′-CTTGGCAGCCTTCCTGATT-3′; 5′-GCAAAACTGCACCTTCACAC-3′; SEQ ID NO: 145 SEQ ID NO: 146 CYR615′-CGCTCTGAAGGGGATCTG-3′;  5′-ACAGGGTCTGCCCTCTGACT-3′; SEQ ID NO: 147SEQ ID NO: 148 5′-GAGCTCAGTCAGAGGGCAGA-3′;  5′-AACTTTCCCCGTTTTGGTAGA-3′;SEQ ID NO: 149 SEQ ID NO: 150 ITGAV 5′-GACCTTGGAAACCCAATGAA-3′; 5′-TCCATCTCTGACTGCTGGTG-3′; SEQ ID NO: 431 SEQ ID NO: 4325′-GGTGGTATGTGACCTTGGAAA-3′; 5′-GCACACTGAAACGAAGACCA-3′; SEQ ID NO: 439SEQ ID NO: 440 YAP 5′-TGAACAGTGTGGATGAGATGG-3′;5′-GCAGGGTGCTTTGGTTGATA-3′; SEQ ID NO: 151 SEQ ID NO: 152 BGN5′-AAGGGTCTCCAGCACCTCTAC-3′; 5′-AAGGCCTTCTCATGGATCTT-3′; SEQ ID NO: 153SEQ ID NO: 154 5′-GAGCTCCGCAAGGATGACT-3′;  5′-AGGACGAGGGCGTAGAGGT-3′;SEQ ID NO: 155 SEQ ID NO: 156 LAMB1 5′-CATTCAAGGAACCCAGAACC-3′; 5′-GCGTTGAACAAGGTTTCCTC-3′; SEQ ID NO: 157 SEQ ID NO: 158 ITGB35′-AAGAGCCAGAGTGTCCCAAG-3′;  5′-ACTGAGAGCAGGACCACCA-3′; SEQ ID NO: 159SEQ ID NO: 160 5′-CTTCTCCTGTGTCCGCTACAA-3′;  5′-CATGGCCTGAGCACATCTC-3′; SEQ ID NO: 161 SEQ ID NO: 162 5′-TGCCTGCACCTTTAAGAAAGA-3′;5′-CCGGTCAAACTTCTTACACTCC-3′; SEQ ID NO: 163 SEQ ID NO: 1645′-AAGGGGGAGATGTGCTCAG-3′;  5′-CAGTCCCCACAGCTGCAC-3′;  SEQ ID NO: 165SEQ ID NO: 166 CXCL1 5′-AAACCGAAGTCATAGCCACAC-3′;5′-AAGCTTTCCGCCCATTCTT-3′;  SEQ ID NO: 167 SEQ ID NO: 168 THBS25′-AGGCCCAAGACTGGCTACAT-3′;  5′-CTGCCATGACCTGTTTTCCT-3′; SEQ ID NO: 169SEQ ID NO: 170 5′-GGCAGGTGCGAACCTTATG-3′;  5′-CCTTCCAGCCAATGTTCCT-3′; SEQ ID NO: 171 SEQ ID NO: 172 COL18A1 5′-GATCGCTGAGCTGAAGGTG-3′; 5′-CGGATGCCCCATCTGAGT-3′;  SEQ ID NO: 173 SEQ ID NO: 174 SPARC5′-CCCATTGGCGAGTTTGAGAAG-3′; 5′-AGGAAGAGTCGAAGGTCTTGTT-3′;SEQ ID NO: 175 SEQ ID NO: 176 5′-GGAAGAAACTGTGGCAGAGG-3′; 5′-GGACAGGATTAGCTCCCACA-3′; SEQ ID NO: 177 SEQ ID NO: 178 TP535′-ACAACGTTCTGTCCCCCTTG-3′;  5′-GGGGACAGCATCAAATCATC-3′; SEQ ID NO: 179SEQ ID NO: 180 PLOD2 5′-TGGATGCAGATGTTGTTTTGA-3′; 5′-CACAGCTTTCCATGACGAGTT-3′; SEQ ID NO: 181 SEQ ID NO: 1825′-TTGATTGAACAAAACAGAAAGATCA-3′; 5′-TGACGAGTTACAAGAGGAGCAA-3′;SEQ ID NO: 183 SEQ ID NO: 184 CCL2 5′-CTGCTCATAGCAGCCACCTT-3′; 5′-AGGTGACTGGGGCATTGATT-3′; SEQ ID NO: 185 SEQ ID NO: 186 FBLN25′-ACGTGGAGGAGGACACAGAC-3′;  5′-GGAGCCTTCAGGGCTACTTC-3′; SEQ ID NO: 187SEQ ID NO: 188 LAMA1 5′-AGCACTGCCAAAGTGGATG-3′; 5′-TTGTTGACATGGAACAAGACC-3′; SEQ ID NO: 189 SEQ ID NO: 190 THBS45′-GTGGGCTACATCAGGGTACG-3′;  5′-CAGAGTCAGCCACCAACTCA-3′; SEQ ID NO: 191SEQ ID NO: 192 5′-CATCATCTGGTCCAACCTCA-3′;  5′-GTCCTCAGGGATGGTGTCAT-3′;SEQ ID NO: 193 SEQ ID NO: 194 COL1A1 5′-TGACCTCAAGATGTGCCACT-3′;5′-TGGTTGGGGTCAATCCAGTA-3′; SEQ ID NO: 195 SEQ ID NO: 1965′-GATGGATTCCAGTTCGAGTATG-3′; 5′-ATCAGGCGCAGGAAGGTC-3′;  SEQ ID NO: 197SEQ ID NO: 198 ITGA5 5′-CCCAAAAAGAGCGTCAGGT-3′; 5′-TTGTTGACATGGAACAAGACC-3′; SEQ ID NO: 199 SEQ ID NO: 200 TAZ5′-CTTCCTAACAGTCCGCCCTA-3′;  5′-CCCGATCAGCACAGTGATTT-3′; SEQ ID NO: 201SEQ ID NO: 202 POSTN 5′-CTGCTTCAGGGAGACACACC-3′; 5′-TGGCTTGCAACTTCCTCAC-3′;  SEQ ID NO: 203 SEQ ID NO: 2045′-AGGAAGTTGCAAGCCAACAA-3′;  5′-CGACCTTCCCTTAATCGTCTT-3′; SEQ ID NO: 205SEQ ID NO: 206 LOX 5′-GCGGAGGAAAACTGTCTGG-3′; 5′-AAATCTGAGCAGCACCCTGT-3′; SEQ ID NO: 207 SEQ ID NO: 2085′-ATATTCCTGGGAATGGCACA-3′;  5′-CCATACTGTGGTAATGTTGATGA-3′;SEQ ID NO: 209 SEQ ID NO: 210 CSRC 5′-TGTCAACAACACAGAGGGAGA-3′;5′-CACGTAGTTGCTGGGGATGT-3′; SEQ ID NO: 211 SEQ ID NO: 2125′-TGGCAAGATCACCAGACGG-3′;  5′-GGCACCTTTCGTGGTCTCAC-3′; SEQ ID NO: 213SEQ ID NO: 214 LAMA3 5′-CATGTCGTCTTGGCTCACTC-3′; 5′-AAATTCTGGCCCCAACAATAC-3′; SEQ ID NO: 215 SEQ ID NO: 216 CDKN1A5′-CATGTCGTCTTGGCTCACTC-3′;  5′-AAATTCTGGCCCCAACAATAC-3′; SEQ ID NO: 217SEQ ID NO: 218 CDKN2A 5′-AGGAGCCAGCGTCTAGGG-3′; 5′-CTGCCCATCATCATGACCT-3′;  SEQ ID NO: 219 SEQ ID NO: 2205′-AACGCACCGAATAGTTACGG-3′;  5′-CATCATCATGACCTGGATCG-3′; SEQ ID NO: 221SEQ ID NO: 222 ITGA2 5′-CACTGTTACGATTCCCCTGA-3′; 5′-CGGCTTTCTCATCAGGTTTC-3′; SEQ ID NO: 223 SEQ ID NO: 224 LAMC25′-ATTAGACGGCCTCCTGCATC-3′;  5′-AGACCAGCCCCTCTTCATCT-3′; SEQ ID NO: 225SEQ ID NO: 226 PCOLCE2 5′-TACTTGGAAAATCACAGTTCCCG-3′;5′-TGAATCGGAAATTGAGAACGACT-3′; SEQ ID NO: 443 SEQ ID NO: 444 LOXL45′-GGCCCCGGGAATTATATCT-3′;  5′-CCACTTCATAGTGGGGGTTC-3′; SEQ ID NO: 227SEQ ID NO: 228 5′-CTGCACAACTGCCACACAG-3′;  5′-GTTCTGCATTGGCTGGGTAT-3′;SEQ ID NO: 229 SEQ ID NO: 230 PCOLCE 5′-CGTGGCAAGTGAGGGGTTC-3′; 5′-CGAAGACTCGGAATGAGAGGG-3′; SEQ ID NO: 231 SEQ ID NO: 2325′-GAGGCTTCCTGCTCTGGT-3′;  5′-CGCAAAATTGGTGCTCAGT-3′;  SEQ ID NO: 233SEQ ID NO: 234 LAMB3 5′-GTCCGGGACTTCCTAACAGA-3′; 5′-GCTGACCTCCTGGATAGTGG-3′; SEQ ID NO: 235 SEQ ID NO: 236 PMEL5′-GTGGTCAGCACCCAGCTTAT-3′;  5′-CCAAGGCCTGCTTCTTGAC-3′;  SEQ ID NO: 237SEQ ID NO: 238 5′-GCTGTGGTCCTTGCATCTCT-3′;  5′-GCTTCATAAGTCTGCGCCTA-3′;SEQ ID NO: 239 SEQ ID NO: 240 NES 5′-CTTCCCTCAGCTTTCAGGAC-3′; 5′-TCTGGGGTCCTAGGGAATTG-3′; SEQ ID NO: 241 SEQ ID NO: 2425′-ACCTCAAGATGTCCCTCAGC-3′;  5′-CAGGAGGGTCCTGTACGTG-3′; SEQ ID NO: 243SEQ ID NO: 244 LICAM 5′-GAGACCTTCGGCGAGTACAG-3′; 5′-AAAGGCCTTCTCCTCGTTGT-3′; SEQ ID NO: 245 SEQ ID NO: 2465′-GGCGGCAAATACTCAGTGAA-3′;  5′-CCTGGGTGTCCTCCTTATCC-3′; SEQ ID NO: 247SEQ ID NO: 248 GDF15 5′-CGGATACTCACGCCAGAAGT-3′; 5′-AGAGATACGCAGGTGCAGGT-3′; SEQ ID NO: 249 SEQ ID NO: 2505′-AAGATTCGAACACCGACCTC-3′;  5′-GCACTTCTGGCGTGAGTATC-3′; SEQ ID NO: 251SEQ ID NO: 252 ARPC1B 5′-CACGCCTGGAACAAGGAC-3′; 5′-ATGCACCTCATGGTTGTTGG-3′; SEQ ID NO: 253 SEQ ID NO: 2545′-CAGGTGACAGGCATCGACT-3′;  5′-CGCAGGTCACAATACGGTTA-3′; SEQ ID NO: 255SEQ ID NO: 256 FARP1 5′-TGAGGCCCTGAGAGAGAAGA-3′; 5′-ATTCCGAAACTCCACACGTC-3′; SEQ ID NO: 257 SEQ ID NO: 2585′-TCAAGGAAATTGAGCAACGA-3′;  5′-TCTGATTTGGGCATTTGAGC-3′; SEQ ID NO: 259SEQ ID NO: 260 NTRK3 5′-TATGGTCGACGGTCCAAAT-3′; 5′-TCCTCACCACTGATGACAGC-3′; SEQ ID NO: 261 SEQ ID NO: 2625′-CACTGTGACCCACAAACCAG-3′;  5′-GCAAGTCCAACTGCTATGGA-3′; SEQ ID NO: 263SEQ ID NO: 264 CSK 5′-TGAGGCCCTGAGAGAGAAGA-3′; 5′-ATTCCGAAACTCCACACGTC-3′; SEQ ID NO: 265 SEQ ID NO: 2665′-TCTACTCCTTTGGGCGAGTG-3′;  5′-CGTCCTTCAGGGGAATTCTT-3′; SEQ ID NO: 267SEQ ID NO: 268 CD44 5′-TAAGGACACCCCAAATTCCA-3′; 5′-GCCAAGATGATCAGCCATTC-3′; SEQ ID NO: 269 SEQ ID NO: 2705′-GCAGTCAACAGTCGAAGAAGG-3′; 5′-AGCTTTTTCTTCTGCCCACA-3′; SEQ ID NO: 271SEQ ID NO: 272 SNX17 5′-AGCCAGCAAGCAGTGAAGTC-3′; 5′-TCAGGTGACTCAAGCAGTGG-3′; SEQ ID NO: 273 SEQ ID NO: 2745′-CCGGGAGTCTATGGTCAAAC-3′;  5′-CACGGCACTCAGCTTACTTG-3′; SEQ ID NO: 275SEQ ID NO: 276 PLAT 5′-TGGAGCAGTCTTCGTTTCG-3′; 5′-CTGGCTCCTCTTCTGAATCG-3′; SEQ ID NO: 277 SEQ ID NO: 2785′-GCCCGATTCAGAAGAGGAG-3′;  5′-TCATCTCTGCAGATCACTTGG-3′; SEQ ID NO: 279SEQ ID NO: 280

The following was performed to generate a standard curve for the targetof each primer pair. The standard was generated with a defined number ofamplicons per volume for each primer pair. In particular, a standard(S7) was designed to contain about 5 million copies ofamplicon-containing cDNA in a bacterial expression vector backbone(pJET1.2 obtained from Fermentas) per one microliter volume for eachprimer pair. From this, six 1:10 dilutions were generated such thatseven standards S1 to S7 were obtained ranging from 5 to 5 millioncopies of amplicon. To obtain fragments of cDNA, total RNA was extractedfrom the human HaCaT, A431, and A375 cell lines, and the RNA was reversetranscribed into cDNA. Cell line-derived cDNA was used as a template toamplify fragments of cDNA that contained the desired amplicons for thereal time-PCR primer pairs. A list of primers used to generate thedesired cDNA fragments is listed in Table 3.

TABLE 3Primer sets for generating cDNA fragments of the indicated genes.Gene Name Forward primer Reverse primer FN1 5′-CCAGCAGAGGCATAAGGTTC-3′; 5′-AGTAGTGCCTTCGGGACTGG-3′;  SEQ ID NO: 281 SEQ ID NO: 282 SPP15′-AGGCTGATTCTGGAAGTTCTGAGG-3′;  5′-AATCTGGACTGCTTGTGGCTG-3′; SEQ ID NO: 283 SEQ ID NO: 284 COL4A1 5′-GTTGGGCCTCCAGGATTTA-3′; 5′-GCCTGGTAGTCCTGGGAAAC-3′;  SEQ ID NO: 285 SEQ ID NO: 286 TNC5′-TGGATGGATTGTGTTCCTGA-3′;  5′-GCCTGCCTTCAAGATTTCTG-3′;  SEQ ID NO: 287SEQ ID NO: 288 ITGA3 5′-CTGAGACTGTGCTGACCTGTG-3′; 5′-CTCTTCATCTCCGCCTTCTG-3′;  SEQ ID NO: 289 SEQ ID NO: 290 LOXL35′-GAGACCGCCTACATCGAAGA-3′;  5′-GGTAGCGTTCAAACCTCCTG-3′;  SEQ ID NO: 291SEQ ID NO: 292 AGRN 5′-ACACCGTCCTCAACCTGAAG-3′; 5′-AATGGCCAGTGCCACATAGT-3′;  SEQ ID NO: 293 SEQ ID NO: 294 VCAN5′-GGTGCACTTTGTGAGCAAGA-3′;  5′-TTGGTATGCAGATGGGTTCA-3′;  SEQ ID NO: 295SEQ ID NO: 296 PLOD3 5′-AGCTGTGGTCCAACTTCTGG-3′; 5′-GTGTGGTAACCGGGAAACAG-3′;  SEQ ID NO: 297 SEQ ID NO: 298 ITGB15′-TTCAGTTTGCTGTGTGTTTGC-3′;  5′-CCACCTTCTGGAGAATCCAA-3′; SEQ ID NO: 299 SEQ ID NO: 300 PTK2 5′-GGCAGTATTGACAGGGAGGA-3′; 5′-TACTCTTGCTGGAGGCTGGT-3′;  SEQ ID NO: 301 SEQ ID NO: 302 CTGF5′-GCCTATTCTGTCACTTCGGCTC-3′;  5′-GCAGGCACAGGTCTTGATGAAC-3′; SEQ ID NO: 303 SEQ ID NO: 304 PLOD1 5′-GACCTCTGGGAGGTGTTCAG-3′; 5′-TTAGGGATCGACGAAGGAGA-3′;  SEQ ID NO: 305 SEQ ID NO: 306 LAMC15′-ATTCCTGCCATCAACCAGAC-3′;  5′-CCTGCTTCTTGGCTTCATTC-3′;  SEQ ID NO: 307SEQ ID NO: 308 THBS1 5′-CAAAGGGACATCCCAAAATG-3′; 5′-GAGTCAGCCATGATTTTCTTCC-3′;  SEQ ID NO: 309 SEQ ID NO: 310 LOXL25′-TACCCCGAGTACTTCCAGCA-3′;  5′-GATCTGCTTCCAGGTCTTGC-3′;  SEQ ID NO: 311SEQ ID NO: 312 IL6 5′-CACACAGACAGCCACTCACC-3′; 5′-CAGGGGTGGTTATTGCATCT-3′;  SEQ ID NO: 313 SEQ ID NO: 314 LOXL15′-CAGACCCCAACTATGTGCAA-3′;  5′-CGCATTGTAGGTGTCATAGCA-3′; SEQ ID NO: 315 SEQ ID NO: 316 IL8 5′-CTCTCTTGGCAGCCTTCCT-3′; 5′-TGAATTCTCAGCCCTCTTCAA-3′;  SEQ ID NO: 317 SEQ ID NO: 318 CYR615′-TCGCCTTAGTCGTCACCCTT-3′;  5′-TGTTTCTCGTCAACTCCACCTCG-3′; SEQ ID NO: 319 SEQ ID NO: 320 ITGAV 5′-CTGATTTCATCGGGGTTGTC-3′; 5′-TGCCTTGCTGAATGAACTTG-3′;  SEQ ID NO: 321 SEQ ID NO: 322 YAP5′-CCAGTGAAACAGCCACCAC-3′;  5′-CTCCTTCCAGTGTTCCAAGG-3′;  SEQ ID NO: 323SEQ ID NO: 324 BGN 5′-GGACTCTGTCACACCCACCT-3′; 5′-CAGGGTCTCAGGGAGGTCTT-3′;  SEQ ID NO: 325 SEQ ID NO: 326 LAMB15′-TGCCAGAGCTGAGATGTTGTT-3′;  5′-TGTAGCATTTCGGCTTTCCT-3′; SEQ ID NO: 327 SEQ ID NO: 328 ITGB3 5′-GGCAAGTACTGCGAGTGTGA-3′; 5′-ATTCTTTTCGGTCGTGGATG-3′;  SEQ ID NO: 329 SEQ ID NO: 330 CXCL15′-CACTGCTGCTCCTGCTCCT-3′;  5′-TGTTCAGCATCTTTTCGATGA-3′;  SEQ ID NO: 331SEQ ID NO: 332 THBS2 5′-TGACAATGACAACATCCCAGA-3′; 5′-TGAGTCTGCCATGACCTGTT-3′;  SEQ ID NO: 333 SEQ ID NO: 334 COL18A15′-CCCTGCTCTACACAGAACCAG-3′;  5′-ACACCTGGCTCCCCTTTCT-3′;  SEQ ID NO: 335SEQ ID NO: 336 SPARC 5′-GCCTGGATCTTCTTTCTCCTTTGC-3′; 5′-CATCCAGGGCGATGTACTTGTC-3′;  SEQ ID NO: 337 SEQ ID NO: 338 TP535′-CCCCCTCTGAGTCAGGAAAC-3′;  5′-TCATGTGCTGTGACTGCTTG-3′;  SEQ ID NO: 339SEQ ID NO: 340 PLOD2 5′-TGGACCCACCAAGATTCTCCTG-3′; 5′-GACCACAGCTTTCCATGACGAG-3′;  SEQ ID NO: 341 SEQ ID NO: 342 CCL25′-TCTGTGCCTGCTGCTCATAG-3′;  5′-GAGTTTGGGTTTGCTTGTCC-3′;  SEQ ID NO: 343SEQ ID NO: 344 FBLN2 5′-CGAGAAGTGCCCAGGAAG-3′; 5′-AGTGAGAAGCCAGGAAAGCA-3′;  SEQ ID NO: 345 SEQ ID NO: 346 LAMA15′-TGGAAATATCACCCACAGCA-3′;  5′-AGGCATTTTTGCTTCACACC-3′;  SEQ ID NO: 347SEQ ID NO: 348 THBS4 5′-GCTCCAGCTTCTACGTGGTC-3′; 5′-TTAATTATCGAAGCGGTCGAA-3′;  SEQ ID NO: 349 SEQ ID NO: 350 COL1A15′-AGCCAGCAGATCGAGAACAT-3′;  5′-CCTTCTTGAGGTTGCCAGTC-3′;  SEQ ID NO: 351SEQ ID NO: 352 ITGA5 5′-CACCAATCACCCCATTAACC-3′; 5′-GCTTGAGCTGAGCTTTTTCC-3′;  SEQ ID NO: 353 SEQ ID NO: 354 TAZ5′-CCAGGTGCTGGAAAAAGAAG-3′;  5′-GAGCTGCTCTGCCTGAGTCT-3′;  SEQ ID NO: 355SEQ ID NO: 356 POSTN 5′-GCAGACACACCTGTTGGAAA-3′; 5′-GAACGACCTTCCCTTAATCG-3′;  SEQ ID NO: 357 SEQ ID NO: 358 LOX5′-CCTACTACATCCAGGCGTCCAC-3′;  5′-ATGCAAATCGCCTGTGGTAGC-3′; SEQ ID NO: 359 SEQ ID NO: 360 CSRC 5′-CTGTTCGGAGGCTTCAACTC-3′; 5′-AGGGATCTCCCAGGCATC-3′;  SEQ ID NO: 361 SEQ ID NO: 362 LAMAS5′-TACCTGGGATCACCTCCATC-3′;  5′-ACAGGGATCCTCAGTGTCGT-3′;  SEQ ID NO: 363SEQ ID NO: 364 CDKN1A 5′-CGGGATGAGTTGGGAGGAG-3′; 5′-TTAGGGCTTCCTCTTGGAGA-3′;  SEQ ID NO: 365 SEQ ID NO: 366 CDKN2A-5′-ATGGTGCGCAGGTTCTTG-3′;  5′-ACCAGCGTGTCCAGGAAG-3′;  004 2A-201SEQ ID NO: 367 SEQ ID NO: 368 CDKN2A- 5′-GAGCAGCATGGAGCCTTC-3′; 5′-GCATGGTTACTGCCTCTGGT-3′;  001 2A-202 SEQ ID NO: 369 SEQ ID NO: 370ITGA2 5′-CAAACAGACAAGGCTGGTGA-3′;  5′-TCAATCTCATCTGGATTTTTGG-3′; SEQ ID NO: 371 SEQ ID NO: 372 LAMC2 5′-CTGCAGGTGGACAACAGAAA-3′; 5′-CATCAGCCAGAATCCCATCT-3′;  SEQ ID NO: 373 SEQ ID NO: 374 PCOLCE25′-GTCCCCAGAGAGACCTGTTT-3′;  5′-AGACACAATTGGCGCAGGT-3′;  SEQ ID NO: 375SEQ ID NO: 376 LOXL4 5′-AAGACTGGACGCGATAGCTG-3′; 5′-GGTTGTTCCTGAGACGCTGT-3′;  SEQ ID NO: 377 SEQ ID NO: 378 PCOLCE5′-TACACCAGACCCGTGTTCCT-3′;  5′-TCCAGGTCAAACTTCTCGAAGG-3′; SEQ ID NO: 379 SEQ ID NO: 380 LAMBS 5′-CTTCAATGCCCAGCTCCA-3′; 5′-TTCCCAACCACATCTTCCAC-3′;  SEQ ID NO: 381 SEQ ID NO: 382 CSF25′-CTGCTGCTCTTGGGCACT-3′;  5′-CAGCAGTCAAAGGGGATGAC-3′;  SEQ ID NO: 383SEQ ID NO: 384 ACTB 5′-AGGATTCCTATGTGGGCGACG-3′; 5′-TCAGGCAGCTCGTAGCTCTTC-3′;  SEQ ID NO: 385 SEQ ID NO: 386 RPLP05′-GGAATGTGGGCTTTGTGTTCACC-3′;  5′-AGGCCAGGACTCGTTTGTACC-3′; SEQ ID NO: 387 SEQ ID NO: 388 RPL8 5′-ACATCAAGGGCATCGTCAAGG-3′; 5′-TCTCTTTCTCCTGCACAGTCTTGG-3′; SEQ ID NO: 389 SEQ ID NO: 390 B2M5′-TGCTCGCGCTACTCTCTCTTTC-3′;  5′-TCACATGGTTCACACGGCAG-3′; SEQ ID NO: 391 SEQ ID NO: 392 K10 5′-TGGCCTTCTCTCTGGAAATG-3′; 5′-TCATTTCCTCCTCGTGGTTC-3′;  SEQ ID NO: 393 SEQ ID NO: 394 K145′-AGGTGACCATGCAGAACCTC-3′;  5′-CCTCGTGGTTCTTCTTCAGG-3′;  SEQ ID NO: 395SEQ ID NO: 396 MITF 5′-GAAATCTTGGGCTTGATGGA-3′; 5′-CCGAGGTTGTTGTTGAAGGT-3′;  SEQ ID NO: 397 SEQ ID NO: 398 TYR5′-CCATGGATAAAGCTGCCAAT-3′;  5′-GACACAGCAAGCTCACAAGC-3′;  SEQ ID NO: 399SEQ ID NO: 400 MLANA 5′-CACTCTTACACCACGGCTGA-3′; 5′-CATAAGCAGGTGGAGCATTG-3′;  SEQ ID NO: 401 SEQ ID NO: 402 PMEL5′-TTGTCCAGGGTATTGAAAGTGC-3′;  5′-GACAAGAGCAGAAGATGCGGG-3′; SEQ ID NO: 403 SEQ ID NO: 404 NES 5′-GCGTTGGAACAGAGGTTGGAG-3′; 5′-CAGGTGTCTCAAGGGTAGCAGG-3′;  SEQ ID NO: 405 SEQ ID NO: 406 L1CAM5′-CTTCCCTTTCGCCACAGTATG-3′;  5′-CCTCCTTCTCCTTCTTGCCACT-3′; SEQ ID NO: 407 SEQ ID NO: 408 GDF15 5′-AATGGCTCTCAGATGCTCCTGG-3′; 5′-GATTCTGCCAGCAGTTGGTCC-3′;  SEQ ID NO: 409 SEQ ID NO: 410 ARPC1B5′-ACCACAGCTTCCTGGTGGAG-3′;  5′-GAGCGGATGGGCTTCTTGATG-3′; SEQ ID NO: 411 SEQ ID NO: 412 FARP1 5′-AACGTGACCTTGTCTCCCAAC-3′; 5′-GCATGACATCGCCGATTCTT-3′;  SEQ ID NO: 413 SEQ ID NO: 414 NTRK35′-TTCAACAAGCCCACCCACTAC-3′;  5′-GTTCTCAATGACAGGGATGCG-3′; SEQ ID NO: 415 SEQ ID NO: 416 CSK 5′-CATGGAATACCTGGAGGGCAAC-3′; 5′-CAGGTGCCAGCAGTTCTTCAT-3′;  SEQ ID NO: 417 SEQ ID NO: 418 CD445′-TCTCAGAGCTTCTCTACATCAC-3′;  5′-CTGACGACTCCTTGTTCACCA-3′; SEQ ID NO: 419 SEQ ID NO: 420 SNX17 5′-TCACCTCCTCTGTACCATTGC-3′; 5′-CTCATCTCCAATGCCCTCGA-3′;  SEQ ID NO: 421 SEQ ID NO: 422 PLAT5′-TGCAATGAAGAGAGGGCTCTG-3′;  5′-CGTGGCCCTGGTATCTATTTCA-3′; SEQ ID NO: 423 SEQ ID NO: 424

The PCR reactions were performed using a high-fidelity polymerase(product name: Phusion′, obtained from New England Biolabs). PCRamplification products were checked for correct size and subsequentlygel purified using the Qiagen Gel Extraction kit. Purified PCR fragmentswere subcloned into the bacterial expression vector pJET1.2 using acommercially available kit (Fermentas). The subcloned fragments weresubsequently checked by restriction digest and DNA sequencing. Bacterialclones harboring the pJET1.2 expression vector with the correct PCRinsert (containing the desired amplicon for real time PCR primer pairs)were frozen and stored at −80° C. This was done to regenerate the samereal time PCR standards over time.

Bacteria harboring the pJET1.2 expression vector with PCR inserts werecultured to generate sufficient amounts of vector. A small aliquot ofthe total retrieved expression vector with insert was linearized usingthe PvuI-HF restriction enzyme (from New England Biolabs). The digestwas then purified using the Qiagen PCR purification kit. Linearized cDNAwas diluted to a concentration of 20 ng/μL. One μL of each of a total of71 linearized cDNA fragments (each at a 20 ng/μL concentration) weremixed and brought to a final volume of 1 mL to obtain standard S7.

Standard S7 was then diluted six times at a 1:10 ratio to obtainedstandards S1 to S6. Dilution was performed using ultrapure waterobtained from Promega (Cat. No. P1193).

The following was performed to generate cDNA from FFPE samples. FFPEblocks were cut at 20 μm sections using a standard Leica microtome. Forlarge pieces of tissue, 2×20 μm full sections were used for RNAretrieval. For smaller tissues, up to 5×20 μm sections were combined forRNA retrieval. RNA extraction was performed using the Qiagen RNA FFPEretrieval kit and a Qiagen QiaCube extraction robot. 0.5 to 1 μg of RNAwith a 260/280 ratio of greater than 1.8 were transcribed into cDNAusing the BioRad iScript cDNA Synthesis kit. All biospecimens wereannotated with clinical data from Mayo Clinic databases. H&E stainedsections were obtained for each block analyzed and digitalized using ahigh-resolution slide scanner.

Fluidigm RT-PCR was performed using a 96×96 format for high throughputanalysis (i.e., 96 cDNAs were analyzed for 96 markers; 9216 datapoints). The primer pairs and cDNAs were prepared in a 96 well format.Standard curves were calculated for each primer pair. Copy numbers per100,000 housekeeping genes were calculated for each primer pair andaveraged per gene. This was initially done for cDNAs derived fromFFPE-embedded skin. To correct for epidermal cell-derivedcross-contamination, background signal per one copy of K14 (a basalkeratinocyte marker) was calculated from FFPE-embedded normal skinsamples for each primer pair and averaged. Experimental samples werethen normalized first to 100,000 housekeeping genes and thenbackground-corrected for epidermal cross-contamination based on K14 copynumber. In particular, the keratinocyte correction factor used for eachgene is set forth in Table E under the column titled “AVG per copy K14.”

The study design (Example 1) involved a comparison of the expressionprofile of ‘true’ benign pigmented skin lesions (nevi, n=73) with ‘true’malignant melanomas of the skin. The latter comprised i) primary skinmelanomas that were documented to metastasize, either to regional lymphnodes, to other areas of skin (in-transit), or to other organs; and ii)in-transit or comparison of nevi to in-transit melanoma metastases(n=54).

Tables C and D summarize the comparisons of the gene expressions betweenthe 73 benign and 54 metastatic. Table A compares the ranked valuesusing the Wilcoxon rank sum test, and Table E compares the dichotomizedvalues (zero vs. >0) using the chi-square test.

A recursive partitioning approach was used to identify cut-points forthe genes that would discriminate between these two groups. Afterpartitioning the data at a cut-point of 45 for FN1, no furtheradditional splits in the data based on the other genes were identifiedby this method.

Using a cutoff of 45 for FN1, the sensitivity was 92.6%, and thespecificity was 98.6%. These results are provided in Tables 4 and 5along with the next possible cutoff for FN1 at 124

TABLE 4 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 472 76 <45 3.15 56.69 59.84 5.26 94.74 7.41 98.63 FN1 50 1 51 >=45 39.370.79 40.16 98.04 1.96 92.59 1.37 Total 54 73 127 42.52 57.48 100.00

TABLE 5 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 873 81 <124 6.30 57.48 63.78 9.88 90.12 14.81 100.00 FN1 >=124 46 0 4636.22 0.00 36.22 100.00 0.00 85.19 0.00 Total 54 73 127 42.52 57.48100.00

The ability to further discriminate between the groups was assessed byconsidering SPP1 or ITGB3 in addition to FN1.

Benign Vs. Malignant—Option 1 Using FN1 and SPP1 (FIG. 5)

The results are set forth in Table 6.

TABLE 6 RULE for FIG. 5 Malignant Benign FN1 <45 and SPP1 = 0 2 72FN1 >=45 52 1 or (FN1 <45 and SPP1 >0) Total 54 73Benign Vs. Malignant—Option 2 Using FN1 and ITGB3 (FIG. 6)

The results are set forth in Table 7.

TABLE 7 RULE for FIG. 6 Malignant Benign FN1 <45 and ITGB3 = 0 3 72FN1 >=45 51 1 or (FN1 <45 and ITGB3 >0) Total 54 73

If all three genes are included, the rule was as follows:

FN1<45 and SPP1=0 and ITGB3=0 denotes a negative test

-   -   vs.

all other combinations denotes a positive test.

This rule resulted in a specificity of 72/73 (98.6%), and a sensitivityof 53/54 (98.2%) (Table 8). Compared to a rule using FN1 alone, thespecificity stayed the same but the sensitivity increased from 92.6% to98.2% using this new rule.

TABLE 8 FN1 SPP1 ITGB3 malignant Frequency <45 Zero Zero No 72 <45 ZeroZero Yes 1 False Neg ID MM150 (case added from the Breslow file) >=45Zero Zero No 1 False Pos ID N29 >=45 Zero Zero Yes 9 >=45 Zero >0 Yes1 >=45 >0 Zero Yes 18 >=45 >0 >0 Yes 22 <45 Zero >0 Yes 1 <45 >0 ZeroYes 2

The rule was evaluated using 25 additional malignant patients who didnot have mets (from the “Breslow” file). For 19 of these 25 patients,the rule was ‘negative’ (Table 9).

TABLE 9 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 19 <45 >0 Zero 1 >=45Zero Zero 2 >=45 >0 Zero 3 <45 1

The rule also was evaluated using 33 thin melanomas (Table 10). For 25of these 33 patients, the rule was ‘negative’.

TABLE 10 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 25 <45 Zero >0 1 >=45Zero Zero 5 >=45 >0 Zero 2

TABLE C Comparison of gene expression between benign and malignantBenign (N = 73) Malignant (N = 54) p value CXCL1_AVG_NORM <0.0001 N 7354 Mean (SD)  4.8 (18.4) 20.0 (26.1) Median   0.0   10.3 Q1, Q3 0.0, 0.0 0.3, 31.1 Range (0.0-141.7) (0.0-120.4) CSF2_AVG_NORM 0.0482 N 73 54Mean (SD) 10.5 (44.1) 4.3 (8.4) Median   2.5   1.0 Q1, Q3 0.6, 7.0 0.0,4.0 Range (0.0-375.0) (0.0-41.0)  CCL2_AVG_NORM <0.0001 N 73 54 Mean(SD) 37.0 (99.4) 244.2 (360.9) Median   0.0  112.8 Q1, Q3 0.0, 9.1  7.2,342.2 Range (0.0-572.0)  (0.0-1777.1) IL8_AVG_NORM <0.0001 N 73 54 Mean(SD) 125.5 (671.3)  53.2 (160.8) Median   0.0   13.0 Q1, Q3 0.0, 0.0 2.1, 52.5 Range  (0.0-5058.7)  (0.0-1171.7) IL6_AVG_NORM <0.0001 N 7354 Mean (SD)  9.9 (69.1) 21.6 (35.0) Median   0.0   8.8 Q1, Q3 0.0, 0.0 0.3, 25.2 Range (0.0-589.1) (0.0-152.3) ITGA5_AVG_NORM <0.0001 N 73 54Mean (SD) 0.0 (0.0)  9.8 (26.8) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0,7.0 Range (0.0-0.0)  (0.0-168.0) ITGA3_AVG_NORM <0.0001 N 73 54 Mean(SD)  3.2 (27.5) 168.2 (313.4) Median   0.0   50.2 Q1, Q3 0.0, 0.0  2.0,160.5 Range (0.0-235.4)  (0.0-1506.0) ITGA2_AVG_NORM 0.0007 N 73 54 Mean(SD) 0.0 (0.0)  2.6 (10.0) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0Range (0.0-0.0)  (0.0-69.7)  ITGAV_AVG_NORM <0.0001 N 73 54 Mean (SD) 3.3 (23.9) 22.0 (32.9) Median   0.0   8.0 Q1, Q3 0.0, 0.0  0.0, 31.0Range (0.0-199.9) (0.0-176.8) ITGB3_AVG_NORM <0.0001 N 73 54 Mean (SD)0.0 (0.0) 43.6 (90.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0  0.0, 52.5Range (0.0-0.0)  (0.0-495.3) ITGB1_AVG_NORM <0.0001 N 73 54 Mean (SD)29.9 (95.1) 616.2 (742.2) Median   0.0  400.2 Q1, Q3 0.0, 0.0  84.7,869.0 Range (0.0-487.9)  (0.0-3877.9) FN1_AVG_NORM <0.0001 N 73 54 Mean(SD)  2.9 (15.6) 1570.9 (1949.8) Median   0.0  898.4 Q1, Q3 0.0, 0.0 299.5, 2186.1 Range (0.0-123.2)  (0.0-11073.5) THBS1_AVG_NORM <0.0001 N73 54 Mean (SD) 0.0 (0.0)  85.1 (136.1) Median   0.0   16.8 Q1, Q3 0.0,0.0  0.0, 153.8 Range (0.0-0.0)  (0.0-786.2) THBS2_AVG_NORM <0.0001 N 7354 Mean (SD)  25.9 (113.4) 280.0 (513.5) Median   0.0   44.1 Q1, Q3 0.0,0.0  0.0, 340.1 Range (0.0-729.2)  (0.0-3030.5) THBS4_AVG_NORM <0.0001 N73 54 Mean (SD)  38.5 (151.2) 228.2 (663.7) Median   0.0   22.5 Q1, Q30.0, 0.0  0.0, 97.9 Range  (0.0-1130.3)  (0.0-3977.7) VCAN_AVG_NORM<0.0001 N 73 54 Mean (SD)  3.0 (21.7) 202.4 (262.8) Median   0.0  103.4Q1, Q3 0.0, 0.0  0.0, 283.5 Range (0.0-181.3)  (0.0-1113.2)BGAN_AVG_NORM <0.0001 N 73 54 Mean (SD)  69.3 (121.0) 422.4 (573.1)Median   0.0  248.5 Q1, Q3  0.0, 97.9 113.5, 462.9 Range (0.0-496.3) (0.0-3348.1) SPP1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 1490.2(3397.4) Median   0.0  338.1 Q1, Q3 0.0, 0.0   4.9, 1577.7 Range(0.0-0.0)   (0.0-22427.0) TNC_AVG_NORM <0.0001 N 73 54 Mean (SD)  66.4(240.1) 800.1 (808.7) Median   0.0  495.8 Q1, Q3 0.0, 0.0  174.5, 1322.9Range  (0.0-1393.3)  (0.0-3162.2) SPARC_AVG_NORM <0.0001 N 73 54 Mean(SD)  843.7 (2222.8) 3208.4 (3182.6) Median   0.0  2895.8  Q1, Q3 0.0,0.0  407.2, 5216.3 Range  (0.0-11175.6)  (0.0-13631.9) AGRN_AVG_NORM<0.0001 N 73 54 Mean (SD)  4.7 (18.1) 51.2 (53.8) Median   0.0   42.1Q1, Q3 0.0, 0.0 10.7, 69.7 Range (0.0-121.7) (0.0-242.0) CTGF_AVG_NORM<0.0001 N 73 54 Mean (SD) 0.4 (3.6)  90.9 (231.6) Median   0.0   22.1Q1, Q3 0.0, 0.0  0.0, 125.9 Range (0.0-30.6)   (0.0-1631.4)CYR61_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.8 (13.0) 27.2 (39.2) Median  0.0   18.7 Q1, Q3 0.0, 0.0  4.9, 32.2 Range (0.0-70.4)  (0.0-267.2)LAMA3_AVG_NORM 0.0004 N 73 54 Mean (SD) 1.1 (9.0) 1.2 (2.9) Median   0.0  0.0 Q1, Q3 0.0, 0.0 0.0, 0.0 Range (0.0-76.8)  (0.0-11.3) LAMC1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  70.6 (159.4) Median  0.0   28.4 Q1, Q3 0.0, 0.0  0.0, 99.3 Range (0.0-0.0)   (0.0-1136.2)LAMB1_AVG_NORM <0.0001 N 73 54 Mean (SD)  9.2 (38.4) 221.1 (354.3)Median   0.0   73.1 Q1, Q3 0.0, 0.0  0.0, 339.8 Range (0.0-248.8) (0.0-1877.6) LAMA1_AVG_NORM <0.0001 N 73 54 Mean (SD)  5.7 (14.5)  65.4(149.0) Median   0.0   10.6 Q1, Q3 0.0, 0.0  0.0, 49.0 Range (0.0-76.5) (0.0-754.3) LAMC2_AVG_NORM 0.0003 N 73 54 Mean (SD) 0.0 (0.0)  4.0(15.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0 Range (0.0-0.0) (0.0-91.1)  LAMB3_AVG_NORM 0.1473 N 73 54 Mean (SD) 33.5 (60.3) 32.2(54.5) Median   0.0   12.1 Q1, Q3  0.0, 44.6  0.0, 37.0 Range(0.0-323.9) (0.0-246.0) COL1A1_AVG_NORM <0.0001 N 73 54 Mean (SD) 1534.4(4365.3) 4191.6 (5865.9) Median   0.0  1704.4  Q1, Q3 0.0, 0.0   0.0,6850.9 Range  (0.0-22510.2)  (0.0-31867.0) COL4A1_AVG_NORM <0.0001 N 7354 Mean (SD) 0.0 (0.0) 211.8 (344.1) Median   0.0  118.4 Q1, Q3 0.0, 0.0 2.3, 261.2 Range (0.0-0.0)   (0.0-1774.4) COL18A1_AVG_NORM <0.0001 N 7354 Mean (SD)  94.2 (783.4) 22.8 (38.8) Median   0.0   4.1 Q1, Q3 0.0,0.0  0.0, 34.4 Range  (0.0-6695.7) (0.0-208.8) LOX_AVG_NORM 0.0003 N 7354 Mean (SD)  37.7 (132.8)  65.0 (113.9) Median   0.0   3.5 Q1, Q3 0.0,0.0  0.0, 58.0 Range (0.0-991.2) (0.0-443.3) LOXL1_AVG_NORM <0.0001 N 7354 Mean (SD) 0.8 (7.1) 39.6 (60.3) Median   0.0   18.5 Q1, Q3 0.0, 0.0 0.0, 65.0 Range (0.0-60.4)  (0.0-349.0) LOXL2_AVG_NORM <0.0001 N 73 54Mean (SD)  43.3 (356.8)  68.5 (129.9) Median   0.0   22.1 Q1, Q3 0.0,0.0  0.0, 89.1 Range  (0.0-3048.4) (0.0-821.4) LOXL3_AVG_NORM <0.0001 N73 54 Mean (SD)  2.2 (12.3) 28.4 (71.1) Median   0.0   9.2 Q1, Q3 0.0,0.0  2.5, 29.4 Range (0.0-89.7)  (0.0-507.5) LOXL4_AVG_NORM 0.0010 N 7354 Mean (SD) 33.8 (91.0) 129.1 (300.4) Median   0.0   9.1 Q1, Q3  0.0,10.2  0.0, 67.0 Range (0.0-529.2)  (0.0-1230.0) PLOD1_AVG_NORM <0.0001 N73 54 Mean (SD)  33.7 (116.5) 420.3 (532.2) Median   0.0  242.3 Q1, Q30.0, 0.0  90.2, 659.3 Range (0.0-878.2)  (0.0-3336.8) PLOD2_AVG_NORM<0.0001 N 73 54 Mean (SD)  44.5 (151.7)  314.8 (1284.4) Median   0.0  53.7 Q1, Q3 0.0, 0.0  2.3, 103.3 Range  (0.0-1124.0)  (0.0-9110.5)PLOD3_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.7 (11.9) 68.0 (81.2) Median  0.0   38.3 Q1, Q3 0.0, 0.0  4.2, 101.9 Range (0.0-87.4)  (0.0-330.2)PCOLCE2_AVG_NORM 0.0010 N 73 54 Mean (SD)  7.7 (25.8)  6.4 (14.9) Median  0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 3.1 Range (0.0-104.8) (0.0-68.4) PCOLCE_AVG_NORM 0.0232 N 73 54 Mean (SD)  92.1 (159.7) 170.4 (339.4)Median   0.0   40.9 Q1, Q3  0.0, 122.2  0.0, 175.1 Range (0.0-699.2) (0.0-1945.2) PTK2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.8 (14.4) 76.6(81.8) Median   0.0   70.0 Q1, Q3 0.0, 0.0  0.0, 127.7 Range (0.0-116.5)(0.0-323.3) CSRC_AVG_NORM 0.0001 N 73 54 Mean (SD) 19.0 (40.9) 45.1(65.9) Median   0.3   19.6 Q1, Q3  0.0, 24.8  4.2, 46.6 Range(0.0-266.6) (0.0-290.2) CDKN1A_AVG_NORM 0.0005 N 73 54 Mean (SD)  78.5(150.9) 181.0 (271.7) Median   0.0   84.2 Q1, Q3  0.0, 118.9  0.0, 253.3Range (0.0-788.2)  (0.0-1083.2) CDKN2A_AVG_NORM 0.0002 N 73 54 Mean (SD) 6.1 (19.6)  9.7 (25.8) Median   0.0   1.0 Q1, Q3 0.0, 0.0 0.0, 6.9Range (0.0-113.2) (0.0-175.1) TP53_AVG_NORM <0.0001 N 73 54 Mean (SD)40.6 (98.6) 231.2 (289.8) Median   0.0  166.9 Q1, Q3 0.0, 0.0  0.0,359.9 Range (0.0-410.8)  (0.0-1722.4) YAP_AVG_NORM <0.0001 N 73 54 Mean(SD)  7.8 (36.6) 112.4 (161.4) Median   0.0   63.1 Q1, Q3 0.0, 0.0  0.0,173.5 Range (0.0-246.3) (0.0-769.0) TAZ_AVG_NORM <0.0001 N 73 54 Mean(SD) 12.2 (27.9) 32.8 (44.3) Median   0.0   15.0 Q1, Q3 0.0, 0.7  0.0,49.0 Range (0.0-122.7) (0.0-186.4) MITF_AVG_NORM <0.0001 N 73 54 Mean(SD) 251.0 (399.5) 569.8 (494.8) Median   45.5  467.3 Q1, Q3  0.0, 331.5184.9, 777.8 Range  (0.0-2143.3)  (0.0-2200.0) MLANA_AVG_NORM 0.1823 N73 54 Mean (SD) 3596.0 (3671.3) 4865.4 (4966.1) Median  2446.8   2803.5 Q1, Q3  950.9, 5019.4 1210.7, 6773.0 Range  (14.0-17180.3) (62.8-19672.1) TYR_AVG_NORM 0.0040 N 73 54 Mean (SD) 349.7 (301.8)839.8 (996.3) Median  254.3  515.1 Q1, Q3 119.5, 527.5  161.0, 1244.9Range  (0.0-1169.8)  (2.0-5500.0) POSTN_AVG_NORM 0.0001 N 73 54 Mean(SD) 1138.7 (2155.7) 1933.9 (2318.1) Median  191.6  1252.0  Q1, Q3  0.0, 1449.9  397.4, 2457.4 Range  (0.0-11078.1)  (0.0-11193.2)FBLN2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.1 (17.3) 26.5 (42.2) Median  0.0   0.0 Q1, Q3 0.0, 0.0  0.0, 48.8 Range (0.0-148.2) (0.0-150.9)

TABLE D Comparison of gene expression between benign and malignantBenign Malignant (N = 73) (N = 54) p value CXCL1_AVG_NORM01 <0.0001 Zero58 (79.5%) 12 (22.2%) >0 15 (20.5%) 42 (77.8%) CSF2_AVG_NORM01 0.0398Zero 15 (20.5%) 20 (37.0%) >0 58 (79.5%) 34 (63.0%) CCL2_AVG_NORM01<0.0001 Zero 53 (72.6%) 12 (22.2%) >0 20 (27.4%) 42 (77.8%)IL8_AVG_NORM01 <0.0001 Zero 63 (86.3%) 10 (18.5%) >0 10 (13.7%) 44(81.5%) IL6_AVG_NORM01 <0.0001 Zero 65 (89.0%) 13 (24.1%) >0  8 (11.0%)41 (75.9%) ITGA5_AVG_NORM01 <0.0001 Zero  73 (100.0%) 38 (70.4%) >0 0(0.0%) 16 (29.6%) ITGA3_AVG_NORM01 <0.0001 Zero 72 (98.6%) 13 (24.1%) >01 (1.4%) 41 (75.9%) ITGA2_AVG_NORM01 0.0007 Zero  73 (100.0%) 46(85.2%) >0 0 (0.0%)  8 (14.8%) ITGAV_AVG_NORM01 <0.0001 Zero 71 (97.3%)24 (44.4%) >0 2 (2.7%) 30 (55.6%) ITGB3_AVG_NORM01 <0.0001 Zero  73(100.0%) 30 (55.6%) >0 0 (0.0%) 24 (44.4%) ITGB1_AVG_NORM01 <0.0001 Zero64 (87.7%) 11 (20.4%) >0  9 (12.3%) 43 (79.6%) FN1_AVG_NORM01 <0.0001Zero 69 (94.5%) 2 (3.7%) >0 4 (5.5%) 52 (96.3%) THBS1_AVG_NORM01 <0.0001Zero  73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30 (55.6%) THBS2_AVG_NORM01<0.0001 Zero 67 (91.8%) 23 (42.6%) >0 6 (8.2%) 31 (57.4%)THBS4_AVG_NORM01 <0.0001 Zero 58 (79.5%) 15 (27.8%) >0 15 (20.5%) 39(72.2%) VCAN_AVG_NORM01 <0.0001 Zero 71 (97.3%) 16 (29.6%) >0 2 (2.7%)38 (70.4%) BGAN_AVG_NORM01 <0.0001 Zero 42 (57.5%)  7 (13.0%) >0 31(42.5%) 47 (87.0%) SPP1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 12(22.2%) >0 0 (0.0%) 42 (77.8%) TNC_AVG_NORM01 <0.0001 Zero 60 (82.2%) 3(5.6%) >0 13 (17.8%) 51 (94.4%) SPARC_AVG_NORM01 <0.0001 Zero 57 (78.1%)13 (24.1%) >0 16 (21.9%) 41 (75.9%) AGRN_AVG_NORM01 <0.0001 Zero 59(80.8%) 5 (9.3%) >0 14 (19.2%) 49 (90.7%) CTGF_AVG_NORM01 <0.0001 Zero72 (98.6%) 21 (38.9%) >0 1 (1.4%) 33 (61.1%) CYR61_AVG_NORM01 <0.0001Zero 56 (76.7%)  9 (16.7%) >0 17 (23.3%) 45 (83.3%) LAMA3_AVG_NORM010.0003 Zero 72 (98.6%) 43 (79.6%) >0 1 (1.4%) 11 (20.4%)LAMC1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30(55.6%) LAMB1_AVG_NORM01 <0.0001 Zero 66 (90.4%) 22 (40.7%) >0 7 (9.6%)32 (59.3%) LAMA1_AVG_NORM01 <0.0001 Zero 57 (78.1%) 16 (29.6%) >0 16(21.9%) 38 (70.4%) LAMC2_AVG_NORM01 0.0003 Zero  73 (100.0%) 45(83.3%) >0 0 (0.0%)  9 (16.7%) LAMB3_AVG_NORM01 0.0061 Zero 45 (61.6%)20 (37.0%) >0 28 (38.4%) 34 (63.0%) COL1A1_AVG_NORM01 <0.0001 Zero 60(82.2%) 17 (31.5%) >0 13 (17.8%) 37 (68.5%) COL4A1_AVG_NORM01 <0.0001Zero  73 (100.0%) 13 (24.1%) >0 0 (0.0%) 41 (75.9%) COL18A1_AVG_NORM01<0.0001 Zero 64 (87.7%) 18 (33.3%) >0  9 (12.3%) 36 (66.7%)LOX_AVG_NORM01 <0.0001 Zero 60 (82.2%) 26 (48.1%) >0 13 (17.8%) 28(51.9%) LOXL1_AVG_NORM01 <0.0001 Zero 72 (98.6%) 23 (42.6%) >0 1 (1.4%)31 (57.4%) LOXL2_AVG_NORM01 <0.0001 Zero 70 (95.9%) 19 (35.2%) >0 3(4.1%) 35 (64.8%) LOXL3_AVG_NORM01 <0.0001 Zero 69 (94.5%) 10 (18.5%) >04 (5.5%) 44 (81.5%) LOXL4_AVG_NORM01 0.0006 Zero 53 (72.6%) 23(42.6%) >0 20 (27.4%) 31 (57.4%) PLOD1_AVG_NORM01 <0.0001 Zero 59(80.8%) 12 (22.2%) >0 14 (19.2%) 42 (77.8%) PLOD2_AVG_NORM01 <0.0001Zero 59 (80.8%) 10 (18.5%) >0 14 (19.2%) 44 (81.5%) PLOD3_AVG_NORM01<0.0001 Zero 66 (90.4%) 11 (20.4%) >0 7 (9.6%) 43 (79.6%)PCOLCE2_AVG_NORM01 0.0002 Zero 66 (90.4%) 34 (63.0%) >0 7 (9.6%) 20(37.0%) PCOLCE_AVG_NORM01 0.0036 Zero 42 (57.5%) 17 (31.5%) >0 31(42.5%) 37 (68.5%) PTK2_AVG_NORM01 <0.0001 Zero 67 (91.8%) 16 (29.6%) >06 (8.2%) 38 (70.4%) CSRC_AVG_NORM01 0.0001 Zero 36 (49.3%)  9 (16.7%) >037 (50.7%) 45 (83.3%) CDKN1A_AVG_NORM01 0.0001 Zero 48 (65.8%) 16(29.6%) >0 25 (34.2%) 38 (70.4%) CDKN2A_AVG_NORM01 <0.0001 Zero 57(78.1%) 23 (42.6%) >0 16 (21.9%) 31 (57.4%) TP53_AVG_NORM01 <0.0001 Zero59 (80.8%) 16 (29.6%) >0 14 (19.2%) 38 (70.4%) YAP_AVG_NORM01 <0.0001Zero 68 (93.2%) 22 (40.7%) >0 5 (6.8%) 32 (59.3%) TAZ_AVG_NORM01 <0.0001Zero 54 (74.0%) 19 (35.2%) >0 19 (26.0%) 35 (64.8%) MITF_AVG_NORM01<0.0001 Zero 26 (35.6%) 2 (3.7%) >0 47 (64.4%) 52 (96.3%)MLANA_AVG_NORM01 >0  73 (100.0%)  54 (100.0%) TYR_AVG_NORM01 0.2202 Zero2 (2.7%) 0 (0.0%) >0 71 (97.3%)  54 (100.0%) POSTN_AVG_NORM01 <0.0001Zero 32 (43.8%) 4 (7.4%) >0 41 (56.2%) 50 (92.6%) FBLN2_AVG_NORM01<0.0001 Zero 71 (97.3%) 31 (57.4%) >0 2 (2.7%) 23 (42.6%)

TABLE E MM79_CN MM80_CN MM81_CN MM82_CN AVG AVG AVG AVG AVG per copy percopy per copy per copy per copy K14 K14 K14 K14 K14 STDEV % STDE

KRT14_AVG_NORM 1 1 1 1 1 0.000 KRT10_AVG_NORM 2.209 2.229 2.92 3.0152.593 0.434 17

MITF_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15

MLANA_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15

TYR_AVG_NORM 0.004 0.002 0.002 0.001 0.002 0.001 56

PMEL_AVG_NORM 0.025 0.027 0.03 0.018 0.025 0.005 20% FN1_AVG_NORM 0.0770.065 0.035 0.042 0.055 0.020 36

SPARC_AVG_NORM 1.294 1.143 0.568 1.707 1.178 0.471 40

AGRN_AVG_NORM 0.004 0.006 0.003 0.002 0.004 0.002 46

THBS1_AVG_NORM 0.064 0.015 0.018 0.005 0.026 0.026 103

THBS2_AVG_NORM 0.366 0.061 0.104 0.057 0.147 0.148 100

THBS4_AVG_NORM 0.018 0.006 0.005 0.001 0.008 0.007 98

VCAN_AVG_NORM 0.095 0.034 0.04 0.027 0.049 0.031 64

BGAN_AVG_NORM 0.015 0.027 0.014 0.015 0.018 0.006 35

COL1A1_AVG_NORM 1.695 3.44 0.689 6.695 3.130 2.635 84

COL4A1_AVG_NORM 0.069 0.026 0.03 0.016 0.035 0.023 66

COL4A2_AVG_NORM 0.115 0.042 0.041 0.004 0.051 0.046 92

COL18A1_AVG_NORM 0.015 0.009 0.005 0.002 0.008 0.006 73

CTGF_AVG_NORM 0.012 0.008 0.016 0.004 0.010 0.005 52

LOX_AVG_NORM 0.029 0.021 0.028 0.021 0.025 0.004 18

LOXL1_AVG_NORM 0.015 0.009 0.016 0.015 0.014 0.003 23

LOXL2_AVG_NORM 0.016 0.011 0.008 0.006 0.010 0.004 42

LOXL3_AVG_NORM 0.003 0.002 0.002 0.001 0.002 0.001 41

LOXL4_AVG_NORM 0.02 0.004 0.003 0.001 0.007 0.009 125

PLOD2_AVG_NORM 0.018 0.014 0.007 0.001 0.010 0.008 75

PLOD1_AVG_NORM 0.069 0.053 0.026 0.017 0.041 0.024 58

SPP1_AVG_NORM 0.092 0.002 0.007 0 0.025 0.045 177

TNC_AVG_NORM 0.025 0.02 0.027 0.013 0.021 0.006 29

PCOLCE2_AVG_NORM 0.011 0.001 0.006 0 0.005 0.005 113

PCOLCE_AVG_NORM 0.028 0.049 0.032 0.04 0.037 0.009 25

PLOD3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113

ITGB3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113

ITGB1_AVG_NORM 0.164 0.054 0.074 0.038 0.083 0.056 68

FBLN2_AVG_NORM 0.049 0.022 0.02 0.016 0.027 0.015 56

CYR61_AVG_NORM 0.006 0.002 0.003 0 0.003 0.003 91

ITGA5_AVG_NORM 0.011 0.005 0.007 0.003 0.007 0.003 53

ITGA3_AVG_NORM 0.016 0.008 0.006 0.008 0.010 0.004 47

ITGA2_AVG_NORM 0.08 0.034 0.019 0.084 0.054 0.033 60

ITGAV_AVG_NORM 0.013 0.005 0.003 0.003 0.006 0.005 79

CSRC_AVG_NORM 0.006 0.003 0.005 0.001 0.004 0.002 59

PTK2_AVG_NORM 0.035 0.02 0.011 0.009 0.019 0.012 63

POSTN_AVG_NORM 0.077 0.092 0.117 0.193 0.120 0.052 43

YAP_AVG_NORM 0.079 0.029 0.033 0.031 0.043 0.024 56

CXCL1_AVG_NORM 0.002 0 0 0 0.001 0.001 200

CSF2_AVG_NORM 0.002 0 0 0 0.001 0.001 200

CCL2_AVG_NORM 0.039 0.018 0.013 0.008 0.020 0.014 70

IL8_AVG_NORM 0.003 0 0.001 0 0.001 0.001 141

IL6_AVG_NORM 0.001 0 0 0 0.000 0.001 200

LAMA3_AVG_NORM 0.038 0.012 0.021 0.011 0.021 0.013 61

TP53_AVG_NORM 0.08 0.04 0.039 0.052 0.053 0.019 36

CDKN1A_AVG_NORM 0.057 0.029 0.037 0.014 0.034 0.018 52

CDKN2A_AVG_NORM 0.003 0.001 0.001 0 0.001 0.001 101

TAZ_AVG_NORM 0.026 0.008 0.008 0.003 0.011 0.010 90

LAMC1_AVG_NORM 0.062 0.013 0.016 0.008 0.025 0.025 101

LAMB1_AVG_NORM 0.046 0.019 0.026 0.008 0.025 0.016 65

LAMA1_AVG_NORM 0.007 0 0.001 0 0.002 0.003 168

LAMC2_AVG_NORM 0.034 0.009 0.012 0.016 0.018 0.011 63

LAMB3_AVG_NORM 0.042 0.016 0.026 0.017 0.025 0.012 48

PLAT_AVG_NORM 0.032 0.02 0.034 0.04 0.032 0.001 27

CSK_AVG_NORM 0.027 0.034 0.021 0.041 0.031 0.001 28

GDF15_AVG_NORM 0.029 0.019 0.033 0.019 0.025 0.001 28

FARP1_AVG_NORM 0.019 0.029 0.022 0.031 0.025 0.001 22

ARPC1B_AVG_NORM 0.015 0.03 0.042 0.018 0.026 0.012 47

NES_AVG_NORM 0.114 0.125 0.112 0.084 0.109 0.017 16

NTRK3_AVG_NORM 0.021 0.025 0.022 0.033 0.025 0.001 25

SNX17_AVG_NORM 0.112 0.099 0.089 0.123 0.106 0.015 14

L1CAM_AVG_NORM 0.017 0.04 0.01 0.024 0.023 0.013 56

CD44_AVG_NORM 0.112 0.089 0.09 0.123 0.104 0.017 16

indicates data missing or illegible when filed

The results provided herein demonstrate the development of a method fordetermining absolute levels (copy numbers) of genes of interest (e.g.,FN-associated genes) from paraffin-embedded tissue by generating ahighly defined internal standard that can be regenerated indefinitely.This standardization approach can allow for the comparison of resultsfrom independent experiments and thus, allows for extensive validation.The RT-PCR not only produced strong signals from highly degraded RNA dueto FFPE embedding, but also was amendable to high-throughput analysisand was highly cost effective. While the methods provided herein werevalidated for melanoma, these methods are likely applicable to otherhuman cancers. The results provided herein also demonstrate thediscrimination between benign and malignant pigmented lesions based onmultiple markers.

Example 3 Additional Marker Panel

A test kit panel was designed to include primers for assessingexpression levels of eight marker genes (ITGB3, TNC, SPP1, SPARC, PLAT,COL4A1, PLOD3, and PTK2) as well as three housekeeping genes (ACTB,RPLP0, and RPL8), one keratinocyte markers (K14) to assess keratinocytecontamination, and two melanocyte markers (MLANA and MITF) to assessmelanocyte content in the skin sections. The primers designed for thiscollection are set forth in Table 11.

TABLE 11 Primers for marker panel kit. SEQ Primer pair  ID Gene nameDirection Sequence NO: ACTB ACTB-G -F TGCTATCCCTGTACGCCTCT 433 ACTB-G -RGAGTCCATCACGATGCCAGT 434 ACTB ACTB-H -F GGACTTCGAGCAAGAGATGG 435 ACTB-H-R CTTCTCCAGGGAGGAGCTG 436 ACTB ACTB-I -F GGCTACAGCTTCACCACCAC 425ACTB-I -R TAATGTCACGCACGATTTCC 426 RPLP0 RPLP0-B -F AACTCTGCATTCTCGCTTCC  9 RPLP0-B -R GCAGACAGACACTGGCAACA  10 RPLP0 RPLP0-C -FGCACCATTGAAATCCTGAGTG  11 RPLP0-C -R GCTCCCACTTTGTCTCCAGT  12 RPL8RPL8-B -F ACAGAGCTGTGGTTGGTGTG  19 RPL8-B -R TTGTCAATTCGGCCACCT  20 RPL8RPL8-E -F ACTGCTGGCCACGAGTACG  17 RPL8-E -R ATGCTCCACAGGATTCATGG  18KRT14 KRT14-D -F TCCGCACCAAGTATGAGACA  39 KRT14-D -RACTCATGCGCAGGTTCAACT  40 KRT14 KRT14-F -F GATGCAGATTGAGAGCCTGA 437KRT14-F -R TTCTTCAGGTAGGCCAGCTC 438 MLANA MLANA-C -FGAGAAAAACTGTGAACCTGTGG  53 MLANA-C -R ATAAGCAGGTGGAGCATTGG  54 MITFMITF-B -F CGGCATTTGTTGCTCAGAAT  47 MITF-B -R GAGCCTGCATTTCAAGTTCC  48ITGB3 ITGB3-A -F AAGAGCCAGAGTGTCCCAAG 159 ITGB3-A -R ACTGAGAGCAGGACCACCA160 ITGB3 ITGB3-B -F CTTCTCCTGTGTCCGCTACAA 161 ITGB3-B -RCATGGCCTGAGCACATCTC 162 PLAT PLAT-C -F CCCAGCCAGGAAATCCAT 427 PLAT-C -RCTGGCTCCTCTTCTGAATCG 428 PLAT PLAT-D -F CAGTGCCTGTCAAAAGTTGC 429 PLAT-D-R CCCCGTTGAAACACCTTG 430 PLAT PLAT-E -F GAAGGATTTGCTGGGAAGTG 441 PLAT-E-R CGTGGCCCTGGTATCTATTT 442 PLOD3 PLOD3-D -F GGAAGGAATCGTGGAGCAG 111PLOD3-D -R CAGCAGTGGGAACCAGTACA 112 PTK2 PTK2-D -F GAGACCATTCCCCTCCTACC119 PTK2-D -R GCTTCTGTGCCATCTCAATCT 120 CDKN2A CDKN2A1-C -FAGGAGCCAGCGTCTAGGG 219 CDKN2A1-C -R CTGCCCATCATCATGACCT 220 CDKN2ACDKN2A2-C -F AACGCACCGAATAGTTACGG 221 CDKN2A2-C -R CATCATCATGACCTGGATCG222

One purpose of the kit was to differentiate between melanoma with highand low risk of regional metastasis, and to appropriately selectpatients for surgical procedures such as sentinel lymph node biopsy(SLNB) or total lymphadenectomy. Another purpose of this kit was toestimate disease-free survival, disease relapse, or likelihood of deathfrom melanoma. To study the ability of these methods to discriminatebetween melanoma with high and low risk of metastasis and to establishsuperiority to established methods, a cohort of 158 patients betweenOctober 1998 and June 2013 were identified as having been diagnosed withhigh-risk melanoma and as having underwent SLNB with the intention toassess metastatic potential of the tumor. Of note, high-risk melanoma bycurrent criteria are defined as melanoma with an invasion depth (Breslowdepth) of ≧1 mm; or melanoma with an invasion depth of 0.75 to 0.99 mmplus the presence of either one of the following three risk factors: >0mitotic figures/mm²; tumor ulceration present; patient age <40 years.

All 158 patients met the criteria for high risk. 136 patients had aBreslow Depth ≧1 mm. 22 patients had a Breslow Depth between 0.75 and0.99 and had at least 1 of the aforementioned 3 risk factors(ulceration, mitotic rate >0, age <40). Of the 158 patients, 36 (22.8%)had a melanoma-positive SLNB.

To select genes for a test kit from a pool of genes, the expressionlevel of 52 genes (variables) was initially determined and dichotomizedas zero vs. >zero and evaluated for an association with positive SLNBusing the chi-square test for a 2×2 contingency level. The genes areordered based on the value of the chi-square test statistic (Table 12).

TABLE 12 Value of the chi-square test statistic variable ITGB3 68.3522SPP1 25.8460 LOXL3 16.7683 PLAT 16.5721 LAMB1 15.7544 YAP 13.4049 PLOD312.6062 TP53 12.3662 COL4A1 11.8336 TNC 11.3862 IL8 10.4697 ITGA510.3561 COL1A1 10.0006 VCAN 9.3250 PLOD1 8.6959 FN1 8.4857 PTK2 7.9874ITGAV 7.7181 LOXL1 7.2109 LOXL2 6.6348 ITGB1 6.3556 CDKN1A 6.3117 CTGF6.2588 GDF15 5.96939 CSRC 5.4435 ITGA2 5.0326 ITGA3 4.0603 LOX 3.8697COL18A1 3.3392 IL6 3.0435 DSPP 2.7822 NTRK3 2.7822 LOXL4 2.7279 THBS22.5110 SPARC 1.9884 PCOLCE2 1.6499 AGRN 1.6118 CXCL1 1.3483 TAZ 1.3458THBS4 1.1281 PCOLCE 0.9198 FBLN2 0.9198 LAMC2 0.9157 CCL2 0.8701 CDKN2A0.6047 CSF2 0.5408 CYR61 0.4713 BGAN 0.4364 LAMA3 0.3455 POSTN 0.1902LAMB3 0.1058 PLOD2 0.0152

As can be deduced from the chi-square test statistic, ITGB3 was highlydiscriminatory between melanoma with and without regional lymph nodemetastasis. The n (%) with a positive SLNB for those with no expressionvs. expression level >0 was summarized (Table 13).

TABLE 13 Overall Positive No. (% of 158) No. (% of each row) FN1_01 Zero110 (69.6%)  18 (16.4%) >0 48 (30.4%) 18 (37.5%) SPP1_01 Zero 93 (58.9%)8 (8.6%) >0 65 (41.1%) 28 (43.1%) ITGB3_01 Zero 107 (67.7%)  4 (3.7%) >051 (32.3%) 32 (62.7%) TNC_01 Zero 114 (72.2%)  18 (15.8%) >0 44 (27.8%)18 (40.9%) PLAT_01 Missing 18  0 Zero 83 (59.3%) 11 (13.3%) >0 57(40.7%) 25 (43.9%) COL4A1_01 Zero 111 (70.3%)  17 (15.3%) >0 47 (29.7%)19 (40.4%) SPARC_01 Missing 4 0 Zero 138 (89.6%) 30 (21.7%) >0 16(10.4%)  6 (37.5%) AGRN_01 Missing 4 0 Zero 23 (14.9%)  3 (13.0%) >0 131(85.1%)  33 (25.2%) THBS1_01 Missing 135  33  Zero 18 (78.3%) 0(0.0%) >0  5 (21.7%)  3 (60.0%) THBS2_01 Missing 4 0 Zero 114 (74.0%) 23 (20.2%) >0 40 (26.0%) 13 (32.5%) THBS4_01 Missing 4 0 Zero 136(88.3%)  30 (22.1%) >0 18 (11.7%)  6 (33.3%) VCAN_01 Missing 4 0 Zero137 (89.0%)  27 (19.7%) >0 17 (11.0%)  9 (52.9%) BGAN_01 Missing 4 0Zero 97 (63.0%) 21 (21.6%) >0 57 (37.0%) 15 (26.3%) COL1A1_01 Missing 40 Zero 145 (94.2%)  30 (20.7%) >0 9 (5.8%)  6 (66.7%) COL18A1_01 Missing4 0 Zero 146 (94.8%)  32 (21.9%) >0 8 (5.2%)  4 (50.0%) CTGF_01 Missing4 0 Zero 128 (83.1%)  25 (19.5%) >0 26 (16.9%) 11 (42.3%) LOX_01 Missing4 0 Zero 149 (96.8%)  33 (22.1%) >0 5 (3.2%)  3 (60.0%) LOXL1_01 Missing4 0 Zero 146 (94.8%)  31 (21.2%) >0 8 (5.2%)  5 (62.5%) LOXL2_01 Missing4 0 Zero 115 (74.7%)  21 (18.3%) >0 39 (25.3%) 15 (38.5%) LOXL3_01Missing 4 0 Zero 67 (43.5%) 5 (7.5%) >0 87 (56.5%) 31 (35.6%) LOXL4_01Missing 4 0 Zero 122 (79.2%)  25 (20.5%) >0 32 (20.8%) 11 (34.4%)PLOD2_01 Missing 4 0 Zero 136 (88.3%)  32 (23.5%) >0 18 (11.7%)  4(22.2%) PLOD1_01 Missing 4 0 Zero 111 (72.1%)  19 (17.1%) >0 43 (27.9%)17 (39.5%) PCOLCE2_01 Missing 4 0 Zero 144 (93.5%)  32 (22.2%) >0 10(6.5%)   4 (40.0%) PCOLCE_01 Missing 4 0 Zero 139 (90.3%)  31 (22.3%) >015 (9.7%)   5 (33.3%) PLOD3_01 Missing 4 0 Zero 109 (70.8%)  17(15.6%) >0 45 (29.2%) 19 (42.2%) ITGB1_01 Missing 4 0 Zero 62 (40.3%)  8(12.9%) >0 92 (59.7%) 28 (30.4%) FBLN2_01 Missing 4 0 Zero 139 (90.3%) 31 (22.3%) >0 15 (9.7%)   5 (33.3%) CYR61_01 Missing 4 0 Zero 50 (32.5%)10 (20.0%) >0 104 (67.5%)  26 (25.0%) ITGA5_01 Missing 4 0 Zero 135(87.7%)  26 (19.3%) >0 19 (12.3%) 10 (52.6%) ITGA3_01 Missing 4 0 Zero56 (36.4%)  8 (14.3%) >0 98 (63.6%) 28 (28.6%) ITGA2_01 Missing 4 0 Zero139 (90.3%)  29 (20.9%) >0 15 (9.7%)   7 (46.7%) ITGAV_01 Missing 4 0Zero 120 (77.9%)  22 (18.3%) >0 34 (22.1%) 14 (41.2%) CSRC_01 Missing 40 Zero 90 (58.4%) 15 (16.7%) >0 64 (41.6%) 21 (32.8%) PTK2_01 Missing 40 Zero 61 (39.6%)  7 (11.5%) >0 93 (60.4%) 29 (31.2%) POSTN_01 Missing 40 Zero 103 (66.9%)  23 (22.3%) >0 51 (33.1%) 13 (25.5%) YAP_01 Missing 40 Zero 137 (89.0%) 26 (19.0%) >0 17 (11.0%) 10 (58.8%) CXCL1_01 Missing4 0 Zero 94 (61.0%) 19 (20.2%) >0 60 (39.0%) 17 (28.3%) CSF2_01 Missing4 0 Zero 131 (85.1%)  32 (24.4%) >0 23 (14.9%)  4 (17.4%) CCL2_01Missing 4 0 Zero 112 (72.7%)  24 (21.4%) >0 42 (27.3%) 12 (28.6%) IL8_01Missing 4 0 Zero 99 (64.3%) 15 (15.2%) >0 55 (35.7%) 21 (38.2%) IL6_01Missing 4 0 Zero 62 (40.3%) 10 (16.1%) >0 92 (59.7%) 26 (28.3%) LAMA3_01Missing 4 0 Zero 148 (96.1%)  34 (23.0%) >0 6 (3.9%)  2 (33.3%) TP53_01Missing 4 0 Zero 125 (81.2%)  22 (17.6%) >0 29 (18.8%) 14 (48.3%)CDKN1A_01 Missing 4 0 Zero 118 (76.6%)  22 (18.6%) >0 36 (23.4%) 14(38.9%) CDKN2A_01 Missing 4 0 Zero 103 (66.9%)  26 (25.2%) >0 51 (33.1%)10 (19.6%) TAZ_01 Missing 4 0 Zero 133 (86.4%)  29 (21.8%) >0 21 (13.6%) 7 (33.3%) LAMC1_01 Missing 136  33  Zero 19 (86.4%) 0 (0.0%) >0  3(13.6%)  3 (100.0%) LAMB1_01 Missing 4 0 Zero 109 (70.8%)  16 (14.7%) >045 (29.2%) 20 (44.4%) LAMA1_01 Missing 4 0 Zero 128 (83.1%)  30(23.4%) >0 26 (16.9%)  6 (23.1%) LAMC2_01 Missing 5 0 Zero 145 (94.8%) 33 (22.8%) >0 8 (5.2%)  3 (37.5%) LAMB3_01 Missing 4 0 Zero 139 (90.3%) 33 (23.7%) >0 15 (9.7%)   3 (20.0%) GDF15_01 Missing 28  4 Zero 65(50.0%) 10 (15.4%) >0 65 (50.0%) 22 (33.8%) DSPP_01 Missing 73 13 Zero16 (18.8%)  7 (43.8%) >0 69 (81.2%) 16 (23.2%) NTRK3_01 Missing 28  4Zero 130 (100.0%) 32 (24.6%)

To formulate a model that distinguishes melanoma that presents withregional metastasis at the time of diagnosis vs. no metastasis, logicregression was used. Logic regression is a machine learning techniquethat uses Boolean explanatory variables. There was not a typicaltechnique to create good cut points for logic regression. To assign cutpoints in the variables, recursive partitioning followed bystandardization of cut point levels was used. These were arbitrarily setat 0, 50, 250, and 500. Cut points derived by logic regression wereadjusted to the next highest standard level. The cut point for ITGB3 wasmaintained at 0. The selected model for predicting metastasis was thefollowing:

IF(OR(ITGB3>0,(AND(OR(PTK2>250,PLAT>500,PLOD3>250),CDKN2A<50)))=TRUEthen predict metastasisCut point ITGB3=0Cut point PLAT=500Cut point PTK2=250Cut point PLOD3=250Cut point CDKN2A=50

As can be seen from the formula, the risk of melanoma metastasis washigh if ITGB3, PLAT, PTK2 or PLOD3 levels are increased and CDKN2A islow.

This model predicted regional metastasis (defined as a positive SLNbiopsy at the time of primary cancer diagnosis) with a specificity of80.3% and sensitivity of 97.3%.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

1. A method for identifying a malignant skin lesion, wherein said methodcomprises: (a) determining, within a test sample, the expression levelof a marker gene selected from the group consisting of PLAT, SPP1, TNC,ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2,PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain a measured expressionlevel of said marker gene for said test sample, (b) determining, withinsaid test sample, the expression level of a keratinocyte marker gene toobtain a measured expression level of said keratinocyte marker gene forsaid test sample, (c) removing, from said measured expression level ofsaid marker gene for said test sample, a level of expressionattributable to keratinocytes present in said test sample using saidmeasured expression level of said keratinocyte marker gene for said testsample and a keratinocyte correction factor to obtain a corrected valueof marker gene expression for said test sample, and (d) identifying saidtest sample as containing a malignant skin lesion based, at least inpart, on said corrected value of marker gene expression for said testsample.
 2. The method of claim 1, wherein said keratinocyte marker geneis K14.
 3. The method of claim 1, wherein said marker gene is SPP1 orITGB3.
 4. The method of claim 1, wherein step (c) comprises (i)multiplying said measured expression level of said keratinocyte markergene for said test sample by said keratinocyte correction factor toobtain a correction value and (ii) subtracting said correction valuefrom said measured expression level of said marker gene for said testsample to obtain said corrected value of marker gene expression for saidtest sample.
 5. The method of claim 1, wherein said marker gene selectedfrom the group consisting of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNCand COL4A1.
 6. The method of claim 1, wherein said marker gene selectedfrom the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2,PLOD3, and SPARC. 7-8. (canceled)
 9. The method of claim 1, wherein saidmethod comprises determining, within said test sample, the expressionlevel of at least seven marker genes selected from the group consistingof PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1,GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtainmeasured expression levels of said at least five marker genes for saidtest sample.
 10. The method of claim 1, wherein said method comprisesdetermining, within said test sample, the expression level of ITGB3,PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
 11. The method of claim1, wherein said method comprises determining, within said test sample,the expression level of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, andSPARC.
 12. A kit for identifying a malignant skin lesion, wherein saidkit comprises: (a) a primer pair for determining, within a test sample,the expression level of a marker gene selected from the group consistingof PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF, VCAN, FARP1,GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 to obtain ameasured expression level of said marker gene for said test sample, and(b) a primer pair for determining, within said test sample, theexpression level of a keratinocyte marker gene to obtain a measuredexpression level of said keratinocyte marker gene for said test sample.13. The kit of claim 12, wherein said keratinocyte marker gene is K14.14. The kit of claim 12, wherein said marker gene is SPP1 or ITGB3. 15.The kit of claim 12, wherein said marker gene selected from the groupconsisting of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.16. The kit of claim 12, wherein said marker gene selected from thegroup consisting of PLAT, SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, andSPARC. 17-18. (canceled)
 19. The kit of claim 12, wherein said kitcomprises primer pairs for determining, within said test sample, theexpression level of at least seven marker genes selected from the groupconsisting of PLAT, SPP1, TNC, ITGB3, COL4A1, CD44, CSK, THBS1, CTGF,VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3, IL8, CDKN2A, and CXCL1 toobtain measured expression levels of said at least five marker genes forsaid test sample.
 20. The kit of claim 12, wherein said kit comprisesprimer pairs for determining, within said test sample, the expressionlevel of ITGB3, PLAT, PTK2, PLOD3, CDKN2A, SPP1, TNC and COL4A1.
 21. Thekit of claim 12, wherein said kit comprises primer pairs fordetermining, within said test sample, the expression level of PLAT,SPP1, TNC, ITGB3, COL4A1, PTK2, PLOD3, and SPARC.
 22. A method foridentifying a malignant skin lesion, wherein said method comprises: (a)determining, within a test sample, the expression level of a marker geneselected from the group consisting of PLAT, SPP1, TNC, ITGB3, COL4A1,CD44, CSK, THBS1, CTGF, VCAN, FARP1, GDF15, ITGB1, PTK2, PLOD3, ITGA3,IL8, and CXCL1 to obtain a measured expression level of said marker genefor said test sample, (b) determining, within said test sample, theexpression level of a keratinocyte marker gene to obtain a measuredexpression level of said keratinocyte marker gene for said test sample,(c) removing, from said measured expression level of said marker genefor said test sample, a level of expression attributable tokeratinocytes present in said test sample using said measured expressionlevel of said keratinocyte marker gene for said test sample and akeratinocyte correction factor to obtain a corrected value of markergene expression for said test sample, and (d) identifying said testsample as containing a malignant skin lesion based, at least in part, onsaid corrected value of marker gene expression for said test sample. 23.The method of claim 22, wherein said keratinocyte marker gene is K14.24. The method of claim 22, wherein said marker gene is SPP1.
 25. Themethod of claim 22, wherein step (c) comprises (i) multiplying saidmeasured expression level of said keratinocyte marker gene for said testsample by said keratinocyte correction factor to obtain a correctionvalue and (ii) subtracting said correction value from said measuredexpression level of said marker gene for said test sample to obtain saidcorrected value of marker gene expression for said test sample.